├── .gitignore
├── LICENSE
├── README.md
├── check_env.py
├── data
    ├── DataTau.html
    ├── data_tau.csv
    ├── data_tau_days.csv
    ├── data_tau_ta.csv
    ├── flatlands.txt
    ├── negative_words.txt
    └── postive_words.txt
├── img
    ├── chunk-segmentation.png
    ├── datatau.png
    ├── date.png
    ├── entity_extraction.png
    ├── gutenberg.png
    ├── ldaformula.png
    ├── nb.png
    ├── punkt.png
    └── title.png
└── notebook
    ├── data-tau
        ├── Acquire.ipynb
        ├── Explore.ipynb
        ├── Model.ipynb
        └── Refine.ipynb
    └── twitter
        ├── Acquire.ipynb
        ├── Explore.ipynb
        ├── Model.ipynb
        ├── Refine.ipynb
        ├── demonetization.csv
        └── tweets.csv


/.gitignore:
--------------------------------------------------------------------------------
  1 | 
  2 | # Created by https://www.gitignore.io/api/macos,python
  3 | 
  4 | ### macOS ###
  5 | *.DS_Store
  6 | .AppleDouble
  7 | .LSOverride
  8 | 
  9 | # Icon must end with two \r
 10 | Icon
 11 | # Thumbnails
 12 | ._*
 13 | # Files that might appear in the root of a volume
 14 | .DocumentRevisions-V100
 15 | .fseventsd
 16 | .Spotlight-V100
 17 | .TemporaryItems
 18 | .Trashes
 19 | .VolumeIcon.icns
 20 | .com.apple.timemachine.donotpresent
 21 | # Directories potentially created on remote AFP share
 22 | .AppleDB
 23 | .AppleDesktop
 24 | Network Trash Folder
 25 | Temporary Items
 26 | .apdisk
 27 | 
 28 | 
 29 | ### Python ###
 30 | # Byte-compiled / optimized / DLL files
 31 | __pycache__/
 32 | *.py[cod]
 33 | *$py.class
 34 | 
 35 | # C extensions
 36 | *.so
 37 | 
 38 | # Distribution / packaging
 39 | .Python
 40 | env/
 41 | build/
 42 | develop-eggs/
 43 | dist/
 44 | downloads/
 45 | eggs/
 46 | .eggs/
 47 | lib/
 48 | lib64/
 49 | parts/
 50 | sdist/
 51 | var/
 52 | *.egg-info/
 53 | .installed.cfg
 54 | *.egg
 55 | 
 56 | # PyInstaller
 57 | #  Usually these files are written by a python script from a template
 58 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 59 | *.manifest
 60 | *.spec
 61 | 
 62 | # Installer logs
 63 | pip-log.txt
 64 | pip-delete-this-directory.txt
 65 | 
 66 | # Unit test / coverage reports
 67 | htmlcov/
 68 | .tox/
 69 | .coverage
 70 | .coverage.*
 71 | .cache
 72 | nosetests.xml
 73 | coverage.xml
 74 | *,cover
 75 | .hypothesis/
 76 | 
 77 | # Translations
 78 | *.mo
 79 | *.pot
 80 | 
 81 | # Django stuff:
 82 | *.log
 83 | local_settings.py
 84 | 
 85 | # Flask stuff:
 86 | instance/
 87 | .webassets-cache
 88 | 
 89 | # Scrapy stuff:
 90 | .scrapy
 91 | 
 92 | # Sphinx documentation
 93 | docs/_build/
 94 | 
 95 | # PyBuilder
 96 | target/
 97 | 
 98 | # Jupyter Notebook
 99 | .ipynb_checkpoints
100 | 
101 | # pyenv
102 | .python-version
103 | 
104 | # celery beat schedule file
105 | celerybeat-schedule
106 | 
107 | # dotenv
108 | .env
109 | 
110 | # virtualenv
111 | .venv/
112 | venv/
113 | ENV/
114 | 
115 | # Spyder project settings
116 | .spyderproject
117 | 
118 | # Rope project settings
119 | .ropeproject
120 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2016 Amit Kapoor
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Text Mining in Python
 2 | 
 3 | ## Topics 
 4 | - Regular Expression
 5 | - Stopword Removal, Stemming
 6 | - Creating features from text
 7 | - Term Frequency and Inverse Document Frequency (TF-IDF)
 8 | - Visualising text data and models
 9 | - Sentiment Analysis (Using Naive Bayes Classifier)
10 | - Topic Modelling - Latent Dirichlet Allocation (LDA)
11 | 


--------------------------------------------------------------------------------
/check_env.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Script to check if the required packages for the workshop are installed 
 3 | 
 4 | Author: Bargava Subramanian
 5 | 
 6 | """
 7 | import sys
 8 | 
 9 | # requirements
10 | has = dict(
11 |     gensim='0.12.4',
12 |     IPython='5.0.0',
13 |     lda='1.0.4',
14 |     matplotlib='1.5.1',   
15 |     numpy='1.11.0',
16 |     pandas='0.19',
17 |     requests='2.11.1',
18 |     scipy='0.17.0',
19 |     seaborn='0.7',
20 |     sklearn='0.18',
21 |     spacy='1.2.0',
22 |     tweepy='3.5.0'
23 | )
24 | 
25 | 
26 | 
27 | returns = 0
28 | 
29 | # check installed packages
30 | for module in has.keys():
31 |     try:
32 |         _module = module.split('-')[-1]
33 |         __module__ = __import__(_module, globals(), locals(), [], 0)
34 |         exec('%s = __module__' % _module)
35 |     except ImportError:
36 |         print("%s:: %s" % (module, sys.exc_info()[1]))
37 |         #run.pop(module, None)
38 |         returns += 1
39 | 
40 | 
41 | # check required versions
42 | from distutils.version import LooseVersion as V
43 | for module,version in has.items():
44 |     try:
45 |         _module = module.split('-')[-1]
46 |         assert V(eval(_module).__version__) >= V(version)
47 |     except NameError:
48 |         pass # failed import
49 |     except AttributeError:
50 |         pass # can't version-check non-standard packages...
51 |     except AssertionError:
52 |         print("%s:: Version >= %s is required" % (module, version))
53 |         returns += 1
54 | 
55 | #Check for image
56 | try:
57 |     from PIL import Image
58 | except:
59 |     print("Image not found. Please install pillow package or Image package")
60 | 
61 | #check for wordcloud
62 | try:
63 |     import wordcloud
64 | except:
65 |     print("wordcloud package not found")
66 | 
67 | 
68 | # final report
69 | if not returns:
70 |     print('-'*50)
71 |     print('OK.  All required items installed.')
72 | 
73 | sys.exit(returns)
74 | 
75 | 
76 | 
77 | 


--------------------------------------------------------------------------------
/data/DataTau.html:
--------------------------------------------------------------------------------
 1 | <html><head><link rel="stylesheet" type="text/css" href="news.css">
 2 | <link rel="shortcut icon" href="http://www.iconj.com/ico/d/x/dxo02ap56v.ico">
 3 | <script>
 4 | function byId(id) {
 5 |   return document.getElementById(id);
 6 | }
 7 | 
 8 | function vote(node) {
 9 |   var v = node.id.split(/_/);   // {'up', '123'}
10 |   var item = v[1]; 
11 | 
12 |   // adjust score
13 |   var score = byId('score_' + item);
14 |   var newscore = parseInt(score.innerHTML) + (v[0] == 'up' ? 1 : -1);
15 |   score.innerHTML = newscore + (newscore == 1 ? ' point' : ' points');
16 | 
17 |   // hide arrows
18 |   byId('up_'   + item).style.visibility = 'hidden';
19 |   byId('down_' + item).style.visibility = 'hidden';
20 | 
21 |   // ping server
22 |   var ping = new Image();
23 |   ping.src = node.href;
24 | 
25 |   return false; // cancel browser nav
26 | } </script><script>
27 | 
28 |   (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
29 |   (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
30 |   m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
31 |   })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
32 | 
33 |   ga('create', 'UA-46326769-1', 'datatau.com');
34 |   ga('send', 'pageview');
35 | 
36 | </script><title>DataTau</title></head><body><center><table border=0 cellpadding=0 cellspacing=0 width="85%" bgcolor=#f6f6ef><tr><td bgcolor=#00b4b4><table border=0 cellpadding=0 cellspacing=0 width="100%" style="padding:2px"><tr><td style="width:18px;padding-right:4px"><a href="http://www.datatau.com"><img src="arc.png" width=18 height=18 style="border:1px #b4b400 solid;"></img></a></td><td style="line-height:12pt; height:10px;"><span class="pagetop"><b><a href="news">DataTau</a></b><img src="s.gif" height=1 width=10><a href="newest">new</a> | <a href="newcomments">comments</a> | <a href="leaders">leaders</a> | <a href="submit">submit</a></span></td><td style="text-align:right;padding-right:4px;"><span class="pagetop"><a href="/x?fnid=fDLzOSbeCa">login</a></span></td></tr></table></td></tr><tr style="height:10px"></tr><tr><td><table border=0 cellpadding=0 cellspacing=0><tr><td align=right valign=top class="title">1.</td><td><center><a id=nil href="vote?for=11989&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11989></span></center></td><td class="title"><a href="https://www.springboard.com/blog/eat-rate-love-an-exploration-of-r-yelp-and-the-search-for-good-indian-food/" rel="nofollow">An Exploration of R, Yelp, and the Search for Good Indian Food</a><span class="comhead"> (springboard.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11989>5 points</span> by <a href="user?id=Rogerh91">Rogerh91</a> 4 hours ago  | <a href="item?id=11989">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">2.</td><td><center><a id=nil href="vote?for=11986&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11986></span></center></td><td class="title"><a href="http://blog.insightdatalabs.com/spark-pipelines-elegant-yet-powerful/" rel="nofollow">Spark Pipelines: Elegant Yet Powerful</a><span class="comhead"> (insightdatalabs.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11986>3 points</span> by <a href="user?id=aouyang1">aouyang1</a> 7 hours ago  | <a href="item?id=11986">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">3.</td><td><center><a id=nil href="vote?for=11973&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11973></span></center></td><td class="title"><a href="https://www.youtube.com/watch?v=KeJINHjyzOU">Deep Advances in Generative Modeling</a><span class="comhead"> (youtube.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11973>7 points</span> by <a href="user?id=gwulfs">gwulfs</a> 13 hours ago  | <a href="item?id=11973">1 comment</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">4.</td><td><center><a id=nil href="vote?for=11980&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11980></span></center></td><td class="title"><a href="http://www.buzzfeed.com/westleyargentum/stuff-vcs-say#.lk1wooEBL" rel="nofollow">Shit VCs Say</a><span class="comhead"> (buzzfeed.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11980>3 points</span> by <a href="user?id=Argentum01">Argentum01</a> 8 hours ago  | <a href="item?id=11980">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">5.</td><td><center><a id=nil href="vote?for=11967&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11967></span></center></td><td class="title"><a href="http://sebastianraschka.com/blog/2015/why-python.html" rel="nofollow">Python, Machine Learning, and Language Wars</a><span class="comhead"> (sebastianraschka.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11967>4 points</span> by <a href="user?id=pmigdal">pmigdal</a> 15 hours ago  | <a href="item?id=11967">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">6.</td><td><center><a id=nil href="vote?for=11975&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11975></span></center></td><td class="title"><a href="https://iamtrask.github.io/2015/07/12/basic-python-network/" rel="nofollow">A Neural Network in 11 lines of Python </a><span class="comhead"> (github.io) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11975>3 points</span> by <a href="user?id=dekhtiar">dekhtiar</a> 13 hours ago  | <a href="item?id=11975">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">7.</td><td><center><a id=nil href="vote?for=11955&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11955></span></center></td><td class="title"><a href="http://setosa.io/ev/markov-chains/">Markov Chains Explained Visually</a><span class="comhead"> (setosa.io) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11955>13 points</span> by <a href="user?id=zeroviscosity">zeroviscosity</a> 1 day ago  | <a href="item?id=11955">1 comment</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">8.</td><td><center><a id=nil href="vote?for=11952&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11952></span></center></td><td class="title"><a href="https://github.com/dodger487/dplython">Dplython: Dplyr for Python</a><span class="comhead"> (github.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11952>13 points</span> by <a href="user?id=thenaturalist">thenaturalist</a> 1 day ago  | <a href="item?id=11952">3 comments</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">9.</td><td><center><a id=nil href="vote?for=11940&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11940></span></center></td><td class="title"><a href="http://research.google.com/pubs/pub41854.html">Inferring causal impact using Bayesian structural time-series models</a><span class="comhead"> (google.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11940>8 points</span> by <a href="user?id=Homunculiheaded">Homunculiheaded</a> 1 day ago  | <a href="item?id=11940">1 comment</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">10.</td><td><center><a id=nil href="vote?for=11948&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11948></span></center></td><td class="title"><a href="http://tech.marksblogg.com/billion-nyc-taxi-rides-spark-emr.html" rel="nofollow">A Billion Taxi Rides on Amazon EMR running Spark</a><span class="comhead"> (marksblogg.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11948>5 points</span> by <a href="user?id=marklit">marklit</a> 1 day ago  | <a href="item?id=11948">1 comment</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">11.</td><td><center><a id=nil href="vote?for=11946&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11946></span></center></td><td class="title"><a href="http://trendct.org/2016/03/18/tutorial-web-scraping-and-mapping-breweries-with-import-io-and-r/" rel="nofollow">Tutorial: Web scraping and mapping breweries with import.io and R</a><span class="comhead"> (trendct.org) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11946>4 points</span> by <a href="user?id=jasdumas">jasdumas</a> 1 day ago  | <a href="item?id=11946">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">12.</td><td><center><a id=nil href="vote?for=11939&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11939></span></center></td><td class="title"><a href="http://yanirseroussi.com/2016/03/20/the-rise-of-greedy-robots/" rel="nofollow">The rise of greedy robots</a><span class="comhead"> (yanirseroussi.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11939>4 points</span> by <a href="user?id=yanir">yanir</a> 2 days ago  | <a href="item?id=11939">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">13.</td><td><center><a id=nil href="vote?for=11905&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11905></span></center></td><td class="title"><a href="https://github.com/jmportilla/Python-for-Algorithms--Data-Structures--and-Interviews">Python for Data Structures, Algorithms, and Interviews</a><span class="comhead"> (github.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11905>18 points</span> by <a href="user?id=kokoubaby">kokoubaby</a> 4 days ago  | <a href="item?id=11905">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">14.</td><td><center><a id=nil href="vote?for=11956&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11956></span></center></td><td class="title"><a href="http://techblog.netflix.com/2016/03/extracting-image-metadata-at-scale.html" rel="nofollow">Extracting image metadata at scale</a><span class="comhead"> (netflix.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11956>2 points</span> by <a href="user?id=zachwill">zachwill</a> 1 day ago  | <a href="item?id=11956">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">15.</td><td><center><a id=nil href="vote?for=11909&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11909></span></center></td><td class="title"><a href="http://blog.datalifebalance.com/lift-charts-a-data-scientists-secret-weapon/">Lift charts - A data scientist's secret weapon</a><span class="comhead"> (datalifebalance.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11909>14 points</span> by <a href="user?id=datenheini">datenheini</a> 4 days ago  | <a href="item?id=11909">2 comments</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">16.</td><td><center><a id=nil href="vote?for=11934&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11934></span></center></td><td class="title"><a href="http://swanintelligence.com/how-to-become-a-machine-learning-expert-in-one-simple-step.html" rel="nofollow">How To Become A Machine Learning Expert In One Simple Step</a><span class="comhead"> (swanintelligence.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11934>4 points</span> by <a href="user?id=swanint">swanint</a> 2 days ago  | <a href="item?id=11934">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">17.</td><td><center><a id=nil href="vote?for=11910&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11910></span></center></td><td class="title"><a href="http://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/">Engineers Shouldn’t Write ETL: High Functioning Data Science Departments</a><span class="comhead"> (stitchfix.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11910>10 points</span> by <a href="user?id=legel">legel</a> 4 days ago  | <a href="item?id=11910">3 comments</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">18.</td><td><center><a id=nil href="vote?for=11937&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11937></span></center></td><td class="title"><a href="http://www.willmcginnis.com/2016/03/15/simple-estimation-hierarchical-events-petersburg/" rel="nofollow">Simple estimation of hierarchical events with petersburg</a><span class="comhead"> (willmcginnis.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11937>3 points</span> by <a href="user?id=wdm0006">wdm0006</a> 2 days ago  | <a href="item?id=11937">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">19.</td><td><center><a id=nil href="vote?for=11938&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11938></span></center></td><td class="title"><a href="item?id=11938">Data Science Side Project</a></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11938>6 points</span> by <a href="user?id=yashpatel5400">yashpatel5400</a> 2 days ago  | <a href="item?id=11938">8 comments</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">20.</td><td><center><a id=nil href="vote?for=11920&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11920></span></center></td><td class="title"><a href="http://multithreaded.stitchfix.com/blog/2016/02/04/computer-vision-state-of-the-art/">Unsupervised Computer Vision: The Current State of the Art</a><span class="comhead"> (stitchfix.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11920>6 points</span> by <a href="user?id=carlosfaham">carlosfaham</a> 3 days ago  | <a href="item?id=11920">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">21.</td><td><center><a id=nil href="vote?for=11882&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11882></span></center></td><td class="title"><a href="https://drive.google.com/file/d/0BxGB59WxQI5oTXpQd09jbVpvalE/view">Data Engineering at Slack: Twelve Mistakes I've Made In My First Three Months</a><span class="comhead"> (google.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11882>14 points</span> by <a href="user?id=gwulfs">gwulfs</a> 6 days ago  | <a href="item?id=11882">2 comments</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">22.</td><td><center><a id=nil href="vote?for=11931&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11931></span></center></td><td class="title"><a href="http://www.randalolson.com/2016/03/11/what-data-visualization-tools-do-rdataisbeautiful-oc-creators-use/" rel="nofollow">What data visualization tools do /r/DataIsBeautiful OC creators use?</a><span class="comhead"> (randalolson.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11931>3 points</span> by <a href="user?id=pmigdal">pmigdal</a> 2 days ago  | <a href="item?id=11931">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">23.</td><td><center><a id=nil href="vote?for=11917&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11917></span></center></td><td class="title"><a href="https://nikolaygrozev.wordpress.com/2015/07/01/reshaping-in-pandas-pivot-pivot-table-stack-and-unstack-explained-with-pictures/">Reshaping in Pandas</a><span class="comhead"> (nikolaygrozev.wordpress.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11917>6 points</span> by <a href="user?id=carlosgg">carlosgg</a> 4 days ago  | <a href="item?id=11917">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">24.</td><td><center><a id=nil href="vote?for=11923&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11923></span></center></td><td class="title"><a href="http://blackboxchallenge.com/eng" rel="nofollow">An unusual interactive machine learning challenge</a><span class="comhead"> (blackboxchallenge.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11923>4 points</span> by <a href="user?id=gglumov">gglumov</a> 3 days ago  | <a href="item?id=11923">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">25.</td><td><center><a id=nil href="vote?for=11922&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11922></span></center></td><td class="title"><a href="http://blog.datumbox.com/datumbox-machine-learning-framework-0-7-0-released/" rel="nofollow">Datumbox Machine Learning Framework 0.7.0 Released</a><span class="comhead"> (datumbox.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11922>4 points</span> by <a href="user?id=datumbox">datumbox</a> 3 days ago  | <a href="item?id=11922">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">26.</td><td><center><a id=nil href="vote?for=11865&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11865></span></center></td><td class="title"><a href="http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html">Data science intro for math/phys background</a><span class="comhead"> (p.migdal.pl) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11865>14 points</span> by <a href="user?id=pmigdal">pmigdal</a> 7 days ago  | <a href="item?id=11865">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">27.</td><td><center><a id=nil href="vote?for=11837&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11837></span></center></td><td class="title"><a href="http://lumiverse.io/series/neural-networks-demystified">Neural Networks demystified</a><span class="comhead"> (lumiverse.io) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11837>16 points</span> by <a href="user?id=elyase">elyase</a> 8 days ago  | <a href="item?id=11837">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">28.</td><td><center><a id=nil href="vote?for=11880&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11880></span></center></td><td class="title"><a href="http://insighthealthdata.com/blog/HealthyBeats/">What machines can learn from Apple Watch: detecting undiagnosed heart condition</a><span class="comhead"> (insighthealthdata.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11880>9 points</span> by <a href="user?id=koukouhappy">koukouhappy</a> 6 days ago  | <a href="item?id=11880">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">29.</td><td><center><a id=nil href="vote?for=11862&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11862></span></center></td><td class="title"><a href="http://blog.dominodatalab.com/open-source-winning-against-proprietary-data-science-vendors/">Data Science Tools: The Biggest Winners and Losers</a><span class="comhead"> (dominodatalab.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11862>12 points</span> by <a href="user?id=AnnaOnTheWeb">AnnaOnTheWeb</a> 7 days ago  | <a href="item?id=11862">discuss</a></td></tr><tr style="height:5px"></tr><tr><td align=right valign=top class="title">30.</td><td><center><a id=nil href="vote?for=11868&dir=up&whence=%6e%65%77%73"><img src="grayarrow.gif" border=0 vspace=3 hspace=2></a><span id=down_11868></span></center></td><td class="title"><a href="https://medium.com/@thomasrorystone/10-years-of-open-source-machine-learning-64bb6fb18eb2#.vp3r4try5">10 Years of Open Source Machine Learning</a><span class="comhead"> (medium.com) </span></td></tr><tr><td colspan=2></td><td class="subtext"><span id=score_11868>9 points</span> by <a href="user?id=tstonez">tstonez</a> 6 days ago  | <a href="item?id=11868">1 comment</a></td></tr><tr style="height:5px"></tr><tr style="height:10px"></tr><tr><td colspan=2></td><td class="title"><a href="/x?fnid=CSS821ucAs" rel="nofollow">More</a></td></tr></table></td></tr><tr><td><img src="s.gif" height=10 width=0><table width="100%" cellspacing=0 cellpadding=1><tr><td bgcolor=#00b4b4></td></tr></table><br>
37 | <center></center></td></tr></table></center><center><a href="http://www.datatau.com/rss">RSS
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  1 | title,date
  2 | "An Exploration of R, Yelp, and the Search for Good Indian Food",5 points by Rogerh91 6 hours ago  | discuss
  3 | Deep Advances in Generative Modeling,7 points by gwulfs 15 hours ago  | 1 comment
  4 | Spark Pipelines: Elegant Yet Powerful,3 points by aouyang1 9 hours ago  | discuss
  5 | Shit VCs Say,3 points by Argentum01 10 hours ago  | discuss
  6 | "Python, Machine Learning, and Language Wars",4 points by pmigdal 17 hours ago  | discuss
  7 | A Neural Network in 11 lines of Python ,3 points by dekhtiar 14 hours ago  | discuss
  8 | Markov Chains Explained Visually,13 points by zeroviscosity 1 day ago  | 1 comment
  9 | Dplython: Dplyr for Python,13 points by thenaturalist 1 day ago  | 3 comments
 10 | Inferring causal impact using Bayesian structural time-series models,8 points by Homunculiheaded 1 day ago  | 1 comment
 11 | A Billion Taxi Rides on Amazon EMR running Spark,5 points by marklit 1 day ago  | 1 comment
 12 | Tutorial: Web scraping and mapping breweries with import.io and R,4 points by jasdumas 1 day ago  | discuss
 13 | The rise of greedy robots,4 points by yanir 2 days ago  | discuss
 14 | "Python for Data Structures, Algorithms, and Interviews",18 points by kokoubaby 4 days ago  | discuss
 15 | Extracting image metadata at scale,2 points by zachwill 1 day ago  | discuss
 16 | Lift charts - A data scientist's secret weapon,14 points by datenheini 4 days ago  | 2 comments
 17 | Data Science Side Project,7 points by yashpatel5400 2 days ago  | 9 comments
 18 | How To Become A Machine Learning Expert In One Simple Step,4 points by swanint 2 days ago  | discuss
 19 | Engineers Shouldn’t Write ETL: High Functioning Data Science Departments,10 points by legel 4 days ago  | 3 comments
 20 | Simple estimation of hierarchical events with petersburg,3 points by wdm0006 2 days ago  | discuss
 21 | Unsupervised Computer Vision: The Current State of the Art,6 points by carlosfaham 3 days ago  | discuss
 22 | Data Engineering at Slack: Twelve Mistakes I've Made In My First Three Months,14 points by gwulfs 6 days ago  | 2 comments
 23 | What data visualization tools do /r/DataIsBeautiful OC creators use?,3 points by pmigdal 2 days ago  | discuss
 24 | Reshaping in Pandas,6 points by carlosgg 4 days ago  | discuss
 25 | An unusual interactive machine learning challenge,4 points by gglumov 3 days ago  | discuss
 26 | Datumbox Machine Learning Framework 0.7.0 Released,4 points by datumbox 3 days ago  | discuss
 27 | Data science intro for math/phys background,14 points by pmigdal 7 days ago  | discuss
 28 | Neural Networks demystified,16 points by elyase 8 days ago  | discuss
 29 | What machines can learn from Apple Watch: detecting undiagnosed heart condition,9 points by koukouhappy 6 days ago  | discuss
 30 | Data Science Tools: The Biggest Winners and Losers,12 points by AnnaOnTheWeb 7 days ago  | discuss
 31 | 10 Years of Open Source Machine Learning,9 points by tstonez 6 days ago  | 1 comment
 32 | Has your conversion rate changed? Bayesian timeseries analysis with Python,12 points by yummyfajitas 8 days ago  | discuss
 33 | Do jobs run in families?,5 points by Anon84 5 days ago  | 1 comment
 34 | Introduction to Scikit Flow -  Simplified Interface to TensorFlow,8 points by lefish 7 days ago  | discuss
 35 | "XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow",8 points by crowwork 8 days ago  | discuss
 36 | How to learn machine learning?,8 points by kiechu 8 days ago  | 1 comment
 37 | The Deep Roots of Javascript Fatigue,5 points by nikkielizdemere 6 days ago  | 1 comment
 38 | How do we make Data Tau work?,27 points by hal8 9 days ago  | 18 comments
 39 | "Machine Learning: An In-Depth, Non-Technical Guide — Part 4",7 points by innoarchitech 8 days ago  | discuss
 40 | Data Science Slack channel - Click for invite,7 points by jyotsna 8 days ago  | discuss
 41 | [Ask DT] What are some rookie mistakes in R?,3 points by HKtemp 3 days ago  | discuss
 42 | "Playing ""Moneyball"" on EA FIFA 16",16 points by aabb13 13 days ago  | 3 comments
 43 | Intellexer - Natural Language Processing and Text Mining REST API,16 points by j_downer 13 days ago  | discuss
 44 | Descriptive Statistics in SQL,5 points by nickhould 7 days ago  | discuss
 45 | Genomic Data Visualization using Python,2 points by RadhouaneAniba 4 days ago  | discuss
 46 | How to Use Cohort Data to Analyze User Behavior,2 points by clevertap 4 days ago  | discuss
 47 | Making transparent how variations in analytical choices affect results,4 points by rahmaniacc 7 days ago  | discuss
 48 | Show DT: Datasets.co - An easy way to share and discover ml datasets,2 points by mrborgen86 4 days ago  | discuss
 49 | Is Scala a better choice than Python for Apache Spark?,7 points by srinify 10 days ago  | 1 comment
 50 | Julia: A Fast Language for Numerical Computing,7 points by srinify 10 days ago  | 1 comment
 51 | "An Ode To The Rice Cooker, The Smartest Kitchen Appliance I’ve Ever Owned",2 points by tfturing 4 days ago  | discuss
 52 | Computing Classification Evaluation Metrics in R,4 points by lefish 7 days ago  | discuss
 53 | Analyzing Golden State Warriors' passing network using GraphFrames in Spark,3 points by yukiegosapporo 6 days ago  | discuss
 54 | Megaman: Manifold Learning with Millions of points,4 points by dperry 8 days ago  | 3 comments
 55 | How to Detect Outliers on Parametric and Non Parametric Methods,2 points by clevertap 5 days ago  | discuss
 56 | BallR: Interactive NBA Shot Charts with R and Shiny,12 points by carlosgg 14 days ago  | discuss
 57 | A Billion Taxi Rides on Amazon EMR Running Presto,4 points by marklit 8 days ago  | discuss
 58 | Minecraft to run artificial intelligence experiments,4 points by bsadeghi 8 days ago  | discuss
 59 | Deep Q-Learning (Space Invaders),4 points by pmigdal 8 days ago  | discuss
 60 | Theano Tutorial,2 points by pmigdal 5 days ago  | discuss
 61 | The Personality Space of Cartoon Characters,3 points by lefish 7 days ago  | discuss
 62 | Announcing Apache Flink 1.0.0,11 points by mxm 14 days ago  | discuss
 63 | "Telemetry with Collectd, Logstash, Elasticsearch and Grafana (ELG)",3 points by helloanand 7 days ago  | discuss
 64 | Statisticians Agree: It’s Time To Stop Misusing P-Value,10 points by jpiburn 15 days ago  | 5 comments
 65 | Bayesian Reasoning in The Twilight Zone!,2 points by Homunculiheaded 6 days ago  | discuss
 66 | Bayesian Estimation of G Train Wait Times,7 points by jamesdreiss 12 days ago  | discuss
 67 | XGBoost: A Scalable Tree Boosting System article,6 points by tfturing 12 days ago  | discuss
 68 | Some experiments into explaining complex black box ensemble predictions,2 points by lefish 6 days ago  | discuss
 69 | Creating a Hadoop Pseudo-Distributed Environment,2 points by lefish 6 days ago  | discuss
 70 | "Data Science Pop-Up in Austin, TX",2 points by AnnaOnTheWeb 6 days ago  | discuss
 71 | Train your own image classifier with Inception in TensorFlow,7 points by elyase 13 days ago  | discuss
 72 | Shiny app for running a Tensorflow demo,3 points by shinyman 9 days ago  | discuss
 73 | File details and owners with gitnoc and git-pandas,3 points by wdm0006 9 days ago  | discuss
 74 | 7 Big Data Technologies and When to Use Them that All Data Engineers Should Know,2 points by galvanize 7 days ago  | discuss
 75 | Topic clusters with TF-IDF vectorization with Spark and Scala,2 points by lefish 7 days ago  | discuss
 76 | Neural Doodles: Workflows for the Next Generation of Artists,5 points by pmigdal 12 days ago  | discuss
 77 | Graph Databases 101,5 points by carlosgg 12 days ago  | discuss
 78 | DataRadar.IO - Data Science RSS Feed - Do you have enough data about your data,2 points by dekhtiar 8 days ago  | 3 comments
 79 | International Women's Day: What #PledgeForParity Means To Us,5 points by ddrum001 14 days ago  | discuss
 80 | Top 50 Data Science thought leaders on Twitter,3 points by datawerq 11 days ago  | 3 comments
 81 | Ask DT: Who Is Hiring? (March 2016),27 points by whoishiring 21 days ago  | 15 comments
 82 | Deriving Better Insights From Time Series Data With Cycle Plots,3 points by clevertap 11 days ago  | discuss
 83 | Introducing GraphFrames,7 points by falaki 19 days ago  | discuss
 84 | SQL for Data Analysis,4 points by nickhould 14 days ago  | 6 comments
 85 | Stream processing and messaging systems for the IoT age,3 points by gradientflow 12 days ago  | discuss
 86 | Announcing R Tools for Visual Studio,3 points by brakmic 13 days ago  | discuss
 87 | A simpler way to merge data streams,3 points by apoverton 13 days ago  | discuss
 88 | Optimizing Notification Timing for One Signal,9 points by megandias 26 days ago  | discuss
 89 | Skizze - A high throughput probabilistic data structure service and storage,3 points by seiflotfy 14 days ago  | discuss
 90 | Question: What do you want to say about working with data?,2 points by emiller425 8 days ago  | discuss
 91 | Genomic Ranges - an Introduction to Working with Genomic Data,3 points by AnnaOnTheWeb 13 days ago  | discuss
 92 | TensorFlow for Poets,9 points by ebellm 21 days ago  | 1 comment
 93 | Unsupervised Learning with Even Less Supervision Using Bayesian Optimization,2 points by idewanck 11 days ago  | discuss
 94 | How to work with large JSON datasets using Python and Pandas,9 points by brian_spiering 21 days ago  | discuss
 95 | DrivenData Competition: Model/Visualize Fog Patterns in Morocco,4 points by bull 15 days ago  | discuss
 96 | Deep Learning: Nine Lectures at Collège de France by Yan LeCun,5 points by Anon84 17 days ago  | discuss
 97 | Optimizing Facebook Campaigns with R,2 points by AnnaOnTheWeb 12 days ago  | 1 comment
 98 | "Trump Tweets on a Globe (aka Fun with d3, socket.io, and the Twitter API)",8 points by joelgrus 21 days ago  | discuss
 99 | Why pandas users should be excited about Apache Arrow,17 points by pmigdal 29 days ago  | discuss
100 | Histogram intersection for change detection,8 points by datadive 22 days ago  | discuss
101 | Distributed TensorFlow just open-sourced,10 points by elyase 25 days ago  | discuss
102 | D3.js Screencasts (1 in 3 are free),4 points by Veerle 18 days ago  | discuss
103 | Regression and Classification with Examples in R,5 points by soates 20 days ago  | discuss
104 | Free online course on statistical shape modelling,8 points by shapemean 25 days ago  | discuss
105 | "Don't worry about deep learning, deepen your understanding of causality instead",22 points by yanir 37 days ago  | discuss
106 | Work with private repositories and other updates of the FlyElephant platform,2 points by m31 15 days ago  | discuss
107 | How to import XML to almost anywhere,4 points by Jammink 20 days ago  | discuss
108 | Survival Analysis of Cricket Player Careers,8 points by keshav92 26 days ago  | 6 comments
109 | Generate image analogies using neural matching and blending,2 points by pmigdal 15 days ago  | discuss
110 | "Analyzing 1.8M tweets from Super Bowl 50 (Twython, Twitter API, AYLIEN)",4 points by mikewally 20 days ago  | discuss
111 | Newly released sklearn compatible library of categorical encoders,7 points by wdm0006 25 days ago  | discuss
112 | Watch Tiny Neural Nets Learn,4 points by swanint 21 days ago  | discuss
113 | Four pitfalls of hill climbing: An animated look,5 points by csaid81 23 days ago  | discuss
114 | "Decision Forests, Convolutional Networks and the Models in-Between",2 points by ebellm 16 days ago  | discuss
115 | How a Math Genius Hacked OkCupid to Find True Love,15 points by roh_codeur 34 days ago  | discuss
116 | No developers for PyLearn2,3 points by tfturing 19 days ago  | discuss
117 | Density Estimation with Dirichlet Process Mixtures using PyMC3,6 points by MidsizeBlowfish 25 days ago  | discuss
118 | Using survival analysis and git-pandas to estimate code quality,3 points by wdm0006 20 days ago  | discuss
119 | An Analysis of the Flint Michigan Water Crisis: Part 1 Initial Corrosivity,3 points by JHorn 20 days ago  | discuss
120 | An Analysis of Republican Twitter Follower Interests,6 points by michelangelo 26 days ago  | discuss
121 | Introduction to ML talk,8 points by cjbayesian 29 days ago  | discuss
122 | GloVe vs word2vec revisited,3 points by pmigdal 20 days ago  | discuss
123 | Overoptimizing: a story about kaggle,4 points by wdm0006 30 days ago  | discuss
124 | Undergrad Data Analysis/Science internships SF Bay?,3 points by tctctc 15 days ago  | 5 comments
125 | The Role of Statistical Significance in Growth Hacking,6 points by rawls234 27 days ago  | discuss
126 | Data Science Course @ Harvard,7 points by rahmaniacc 29 days ago  | 2 comments
127 | Principal Component Projection Without Principal Component Analysis,6 points by genofon 27 days ago  | discuss
128 | "Machine Learning: An In-Depth, Non-Technical Guide - Part 3",7 points by innoarchitech 29 days ago  | discuss
129 | Stochastic Dummy Boosting,2 points by mikeskim 18 days ago  | discuss
130 | Interactive Map: Hong-Kong through The Lense of Instagram,2 points by BrianN 19 days ago  | discuss
131 | Data Science at Monsanto,3 points by doctorcroc 22 days ago  | discuss
132 | Data Science at Instacart,11 points by jeremystan 34 days ago  | 3 comments
133 | Building a Streaming Search Platform,6 points by ddrum001 28 days ago  | discuss
134 | Kafka Producer Latency with Large Topic Counts,3 points by marklit 26 days ago  | discuss
135 | A Sneak Peak of the Cloud: the 2 Minute Intro for Beginners,2 points by andymaheshw 20 days ago  | discuss
136 | Win-Vector video courses: price/status changes,2 points by jmount 20 days ago  | discuss
137 | 50+ Data Science and Machine Learning Cheat Sheets,20 points by elyase 42 days ago  | 1 comment
138 | One More Reason Not To Be Scared of Deep Learning,2 points by amplifier_khan 21 days ago  | discuss
139 | Visual Logic Authoring vs Code,2 points by AnnaOnTheWeb 21 days ago  | discuss
140 | Data Science in Python online training with hands-on experience,2 points by Puneet 21 days ago  | discuss
141 | Viewing the US Presidential Primary Through the Lens of Twitter,8 points by michelangelo 33 days ago  | discuss
142 | Caffe on Spark open sourced,4 points by rahmaniacc 27 days ago  | discuss
143 | The Ethical Data Scientist,5 points by tfturing 29 days ago  | discuss
144 | Answers to Frequently Asked Questions in Machine Learning,3 points by rasbt 21 days ago  | discuss
145 | Intro to A/B Testing and P-Values,2 points by randyzwitch 22 days ago  | discuss
146 | Visualizing State Level Data With R and Statebins,2 points by usujason 22 days ago  | discuss
147 | "Probabilistic Graphical Models slides & video lectures (Eric Xing, CMU)",4 points by ororm 28 days ago  | discuss
148 | Sense2vec with spaCy and Gensim,9 points by elyase 36 days ago  | 2 comments
149 | A Billion NYC Taxi and Uber Rides in AWS Redshift,3 points by marklit 31 days ago  | discuss
150 | How to Code and Understand DeepMind's Neural Stack Machine (in Python),2 points by genofon 23 days ago  | discuss
151 | How to make polished Jupyter presentations with optional code visibility,9 points by csaid81 36 days ago  | discuss
152 | How to become a Bayesian in eight easy steps,17 points by EtzA 44 days ago  | 1 comment
153 | Optimizing .*:  Details of Vectorization and Metaprogramming in Julia,4 points by randyzwitch 29 days ago  | discuss
154 | IBM certified Apache Spark Online Training,8 points by divya_jain 36 days ago  | discuss
155 | Geographic Data Science course,2 points by rk 25 days ago  | discuss
156 | "The Daily Mail Stole My Visualization, Twice",5 points by thehoff 32 days ago  | 1 comment
157 | Ensemble Methods: Improved Machine Learning Results,9 points by PyBloggers 38 days ago  | discuss
158 | Apache Spark and unsupervised learning in security,2 points by gradientflow 26 days ago  | discuss
159 | MachineJS: Automated machine learning- just give it a data file!,2 points by dsernst 26 days ago  | discuss
160 | The NSA’s SKYNET program may be killing thousands of innocent people,6 points by zlipp 35 days ago  | discuss
161 | "Big Dimensions, and What You Can Do About It",2 points by ramsey 27 days ago  | discuss
162 | Automate Your Oscars Pool with R,2 points by jamesdreiss 27 days ago  | discuss
163 | Signal Processing with LIGO GW150914 data,9 points by tfturing 39 days ago  | discuss
164 | Overview of DeZyre and Coursera Data Science Course,5 points by ann928 34 days ago  | discuss
165 | Upcoming Datathon in NYC,2 points by VicTrey 28 days ago  | discuss
166 | Summarizing Data in SQL,15 points by elisebreda 46 days ago  | discuss
167 | A/B Testing for Scammers,2 points by sameermanek 28 days ago  | discuss
168 | Highly interpretable classifiers for scikit learn using Bayesian decision rules,2 points by mcnulty 28 days ago  | discuss
169 | Auto-scaling scikit-learn with Spark,11 points by falaki 43 days ago  | discuss
170 | Where the f*** can I park?,2 points by manugarri 29 days ago  | discuss
171 | "Machine Learning: An In-Depth, Non-Technical Guide - Part 2",5 points by innoarchitech 36 days ago  | discuss
172 | Webhose.io now offers a historical data archive,7 points by databuffer 40 days ago  | discuss
173 | Meetup: Introduction to Machine Learning Algorithms for Data Science.,4 points by ann928 36 days ago  | discuss
174 | Exploring the Limits of Language Modeling,8 points by soates 42 days ago  | discuss
175 | Text Mining South Park,7 points by pmigdal 41 days ago  | discuss
176 | Finding the K in K-means by Parametric Bootstrap,7 points by jmount 42 days ago  | 1 comment
177 | Getting Started with Statistics for Data Science,3 points by nickhould 35 days ago  | discuss
178 | Rodeo 1.3 - Tab-completion for docstrings,3 points by glamp 35 days ago  | discuss
179 | Teaching D3.js - links,3 points by pmigdal 35 days ago  | discuss
180 | Parallel scikit-learn on YARN,5 points by stijntonk 39 days ago  | discuss
181 | Meetup: Free Live Webinar on Prescriptive Analytics for Fun and Profit,2 points by ann928 32 days ago  | discuss
182 | 


--------------------------------------------------------------------------------
/data/data_tau_days.csv:
--------------------------------------------------------------------------------
  1 | title,date,days
  2 | "An Exploration of R, Yelp, and the Search for Good Indian Food",5 points by Rogerh91 6 hours ago  | discuss,1
  3 | Deep Advances in Generative Modeling,7 points by gwulfs 15 hours ago  | 1 comment,1
  4 | Spark Pipelines: Elegant Yet Powerful,3 points by aouyang1 9 hours ago  | discuss,1
  5 | Shit VCs Say,3 points by Argentum01 10 hours ago  | discuss,1
  6 | "Python, Machine Learning, and Language Wars",4 points by pmigdal 17 hours ago  | discuss,1
  7 | A Neural Network in 11 lines of Python ,3 points by dekhtiar 14 hours ago  | discuss,1
  8 | Markov Chains Explained Visually,13 points by zeroviscosity 1 day ago  | 1 comment,1
  9 | Dplython: Dplyr for Python,13 points by thenaturalist 1 day ago  | 3 comments,1
 10 | Inferring causal impact using Bayesian structural time-series models,8 points by Homunculiheaded 1 day ago  | 1 comment,1
 11 | A Billion Taxi Rides on Amazon EMR running Spark,5 points by marklit 1 day ago  | 1 comment,1
 12 | Tutorial: Web scraping and mapping breweries with import.io and R,4 points by jasdumas 1 day ago  | discuss,1
 13 | The rise of greedy robots,4 points by yanir 2 days ago  | discuss,2
 14 | "Python for Data Structures, Algorithms, and Interviews",18 points by kokoubaby 4 days ago  | discuss,4
 15 | Extracting image metadata at scale,2 points by zachwill 1 day ago  | discuss,1
 16 | Lift charts - A data scientist's secret weapon,14 points by datenheini 4 days ago  | 2 comments,4
 17 | Data Science Side Project,7 points by yashpatel5400 2 days ago  | 9 comments,2
 18 | How To Become A Machine Learning Expert In One Simple Step,4 points by swanint 2 days ago  | discuss,2
 19 | Engineers Shouldn?t Write ETL: High Functioning Data Science Departments,10 points by legel 4 days ago  | 3 comments,4
 20 | Simple estimation of hierarchical events with petersburg,3 points by wdm0006 2 days ago  | discuss,2
 21 | Unsupervised Computer Vision: The Current State of the Art,6 points by carlosfaham 3 days ago  | discuss,3
 22 | Data Engineering at Slack: Twelve Mistakes I've Made In My First Three Months,14 points by gwulfs 6 days ago  | 2 comments,6
 23 | What data visualization tools do /r/DataIsBeautiful OC creators use?,3 points by pmigdal 2 days ago  | discuss,2
 24 | Reshaping in Pandas,6 points by carlosgg 4 days ago  | discuss,4
 25 | An unusual interactive machine learning challenge,4 points by gglumov 3 days ago  | discuss,3
 26 | Datumbox Machine Learning Framework 0.7.0 Released,4 points by datumbox 3 days ago  | discuss,3
 27 | Data science intro for math/phys background,14 points by pmigdal 7 days ago  | discuss,7
 28 | Neural Networks demystified,16 points by elyase 8 days ago  | discuss,8
 29 | What machines can learn from Apple Watch: detecting undiagnosed heart condition,9 points by koukouhappy 6 days ago  | discuss,6
 30 | Data Science Tools: The Biggest Winners and Losers,12 points by AnnaOnTheWeb 7 days ago  | discuss,7
 31 | 10 Years of Open Source Machine Learning,9 points by tstonez 6 days ago  | 1 comment,6
 32 | Has your conversion rate changed? Bayesian timeseries analysis with Python,12 points by yummyfajitas 8 days ago  | discuss,8
 33 | Do jobs run in families?,5 points by Anon84 5 days ago  | 1 comment,5
 34 | Introduction to Scikit Flow -  Simplified Interface to TensorFlow,8 points by lefish 7 days ago  | discuss,7
 35 | "XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow",8 points by crowwork 8 days ago  | discuss,8
 36 | How to learn machine learning?,8 points by kiechu 8 days ago  | 1 comment,8
 37 | The Deep Roots of Javascript Fatigue,5 points by nikkielizdemere 6 days ago  | 1 comment,6
 38 | How do we make Data Tau work?,27 points by hal8 9 days ago  | 18 comments,9
 39 | "Machine Learning: An In-Depth, Non-Technical Guide???Part 4",7 points by innoarchitech 8 days ago  | discuss,8
 40 | Data Science Slack channel - Click for invite,7 points by jyotsna 8 days ago  | discuss,8
 41 | [Ask DT] What are some rookie mistakes in R?,3 points by HKtemp 3 days ago  | discuss,3
 42 | "Playing ""Moneyball"" on EA FIFA 16",16 points by aabb13 13 days ago  | 3 comments,13
 43 | Intellexer - Natural Language Processing and Text Mining REST API,16 points by j_downer 13 days ago  | discuss,13
 44 | Descriptive Statistics in SQL,5 points by nickhould 7 days ago  | discuss,7
 45 | Genomic Data Visualization using Python,2 points by RadhouaneAniba 4 days ago  | discuss,4
 46 | How to Use Cohort Data to Analyze User Behavior,2 points by clevertap 4 days ago  | discuss,4
 47 | Making transparent how variations in analytical choices affect results,4 points by rahmaniacc 7 days ago  | discuss,7
 48 | Show DT: Datasets.co - An easy way to share and discover ml datasets,2 points by mrborgen86 4 days ago  | discuss,4
 49 | Is Scala a better choice than Python for Apache Spark?,7 points by srinify 10 days ago  | 1 comment,10
 50 | Julia: A Fast Language for Numerical Computing,7 points by srinify 10 days ago  | 1 comment,10
 51 | "An Ode To The Rice Cooker, The Smartest Kitchen Appliance I?ve Ever Owned",2 points by tfturing 4 days ago  | discuss,4
 52 | Computing Classification Evaluation Metrics in R,4 points by lefish 7 days ago  | discuss,7
 53 | Analyzing Golden State Warriors' passing network using GraphFrames in Spark,3 points by yukiegosapporo 6 days ago  | discuss,6
 54 | Megaman: Manifold Learning with Millions of points,4 points by dperry 8 days ago  | 3 comments,8
 55 | How to Detect Outliers on Parametric and Non Parametric Methods,2 points by clevertap 5 days ago  | discuss,5
 56 | BallR: Interactive NBA Shot Charts with R and Shiny,12 points by carlosgg 14 days ago  | discuss,14
 57 | A Billion Taxi Rides on Amazon EMR Running Presto,4 points by marklit 8 days ago  | discuss,8
 58 | Minecraft to run artificial intelligence experiments,4 points by bsadeghi 8 days ago  | discuss,8
 59 | Deep Q-Learning (Space Invaders),4 points by pmigdal 8 days ago  | discuss,8
 60 | Theano Tutorial,2 points by pmigdal 5 days ago  | discuss,5
 61 | The Personality Space of Cartoon Characters,3 points by lefish 7 days ago  | discuss,7
 62 | Announcing Apache Flink 1.0.0,11 points by mxm 14 days ago  | discuss,14
 63 | "Telemetry with Collectd, Logstash, Elasticsearch and Grafana (ELG)",3 points by helloanand 7 days ago  | discuss,7
 64 | Statisticians Agree: It?s Time To Stop Misusing P-Value,10 points by jpiburn 15 days ago  | 5 comments,15
 65 | Bayesian Reasoning in The Twilight Zone!,2 points by Homunculiheaded 6 days ago  | discuss,6
 66 | Bayesian Estimation of G Train Wait Times,7 points by jamesdreiss 12 days ago  | discuss,12
 67 | XGBoost: A Scalable Tree Boosting System article,6 points by tfturing 12 days ago  | discuss,12
 68 | Some experiments into explaining complex black box ensemble predictions,2 points by lefish 6 days ago  | discuss,6
 69 | Creating a Hadoop Pseudo-Distributed Environment,2 points by lefish 6 days ago  | discuss,6
 70 | "Data Science Pop-Up in Austin, TX",2 points by AnnaOnTheWeb 6 days ago  | discuss,6
 71 | Train your own image classifier with Inception in TensorFlow,7 points by elyase 13 days ago  | discuss,13
 72 | Shiny app for running a Tensorflow demo,3 points by shinyman 9 days ago  | discuss,9
 73 | File details and owners with gitnoc and git-pandas,3 points by wdm0006 9 days ago  | discuss,9
 74 | 7 Big Data Technologies and When to Use Them that All Data Engineers Should Know,2 points by galvanize 7 days ago  | discuss,7
 75 | Topic clusters with TF-IDF vectorization with Spark and Scala,2 points by lefish 7 days ago  | discuss,7
 76 | Neural Doodles: Workflows for the Next Generation of Artists,5 points by pmigdal 12 days ago  | discuss,12
 77 | Graph Databases 101,5 points by carlosgg 12 days ago  | discuss,12
 78 | DataRadar.IO - Data Science RSS Feed - Do you have enough data about your data,2 points by dekhtiar 8 days ago  | 3 comments,8
 79 | International Women's Day: What #PledgeForParity Means To Us,5 points by ddrum001 14 days ago  | discuss,14
 80 | Top 50 Data Science thought leaders on Twitter,3 points by datawerq 11 days ago  | 3 comments,11
 81 | Ask DT: Who Is Hiring? (March 2016),27 points by whoishiring 21 days ago  | 15 comments,21
 82 | Deriving Better Insights From Time Series Data With Cycle Plots,3 points by clevertap 11 days ago  | discuss,11
 83 | Introducing GraphFrames,7 points by falaki 19 days ago  | discuss,19
 84 | SQL for Data Analysis,4 points by nickhould 14 days ago  | 6 comments,14
 85 | Stream processing and messaging systems for the IoT age,3 points by gradientflow 12 days ago  | discuss,12
 86 | Announcing R Tools for Visual Studio,3 points by brakmic 13 days ago  | discuss,13
 87 | A simpler way to merge data streams,3 points by apoverton 13 days ago  | discuss,13
 88 | Optimizing Notification Timing for One Signal,9 points by megandias 26 days ago  | discuss,26
 89 | Skizze - A high throughput probabilistic data structure service and storage,3 points by seiflotfy 14 days ago  | discuss,14
 90 | Question: What do you want to say about working with data?,2 points by emiller425 8 days ago  | discuss,8
 91 | Genomic Ranges - an Introduction to Working with Genomic Data,3 points by AnnaOnTheWeb 13 days ago  | discuss,13
 92 | TensorFlow for Poets,9 points by ebellm 21 days ago  | 1 comment,21
 93 | Unsupervised Learning with Even Less Supervision Using Bayesian Optimization,2 points by idewanck 11 days ago  | discuss,11
 94 | How to work with large JSON datasets using Python and Pandas,9 points by brian_spiering 21 days ago  | discuss,21
 95 | DrivenData Competition: Model/Visualize Fog Patterns in Morocco,4 points by bull 15 days ago  | discuss,15
 96 | Deep Learning: Nine Lectures at Coll?ge de France by Yan LeCun,5 points by Anon84 17 days ago  | discuss,17
 97 | Optimizing Facebook Campaigns with R,2 points by AnnaOnTheWeb 12 days ago  | 1 comment,12
 98 | "Trump Tweets on a Globe (aka Fun with d3, socket.io, and the Twitter API)",8 points by joelgrus 21 days ago  | discuss,21
 99 | Why pandas users should be excited about Apache Arrow,17 points by pmigdal 29 days ago  | discuss,29
100 | Histogram intersection for change detection,8 points by datadive 22 days ago  | discuss,22
101 | Distributed TensorFlow just open-sourced,10 points by elyase 25 days ago  | discuss,25
102 | D3.js Screencasts (1 in 3 are free),4 points by Veerle 18 days ago  | discuss,18
103 | Regression and Classification with Examples in R,5 points by soates 20 days ago  | discuss,20
104 | Free online course on statistical shape modelling,8 points by shapemean 25 days ago  | discuss,25
105 | "Don't worry about deep learning, deepen your understanding of causality instead",22 points by yanir 37 days ago  | discuss,37
106 | Work with private repositories and other updates of the FlyElephant platform,2 points by m31 15 days ago  | discuss,15
107 | How to import XML to almost anywhere,4 points by Jammink 20 days ago  | discuss,20
108 | Survival Analysis of Cricket Player Careers,8 points by keshav92 26 days ago  | 6 comments,26
109 | Generate image analogies using neural matching and blending,2 points by pmigdal 15 days ago  | discuss,15
110 | "Analyzing 1.8M tweets from Super Bowl 50 (Twython, Twitter API, AYLIEN)",4 points by mikewally 20 days ago  | discuss,20
111 | Newly released sklearn compatible library of categorical encoders,7 points by wdm0006 25 days ago  | discuss,25
112 | Watch Tiny Neural Nets Learn,4 points by swanint 21 days ago  | discuss,21
113 | Four pitfalls of hill climbing: An animated look,5 points by csaid81 23 days ago  | discuss,23
114 | "Decision Forests, Convolutional Networks and the Models in-Between",2 points by ebellm 16 days ago  | discuss,16
115 | How a Math Genius Hacked OkCupid to Find True Love,15 points by roh_codeur 34 days ago  | discuss,34
116 | No developers for PyLearn2,3 points by tfturing 19 days ago  | discuss,19
117 | Density Estimation with Dirichlet Process Mixtures using PyMC3,6 points by MidsizeBlowfish 25 days ago  | discuss,25
118 | Using survival analysis and git-pandas to estimate code quality,3 points by wdm0006 20 days ago  | discuss,20
119 | An Analysis of the Flint Michigan Water Crisis: Part 1 Initial Corrosivity,3 points by JHorn 20 days ago  | discuss,20
120 | An Analysis of Republican Twitter Follower Interests,6 points by michelangelo 26 days ago  | discuss,26
121 | Introduction to ML talk,8 points by cjbayesian 29 days ago  | discuss,29
122 | GloVe vs word2vec revisited,3 points by pmigdal 20 days ago  | discuss,20
123 | Overoptimizing: a story about kaggle,4 points by wdm0006 30 days ago  | discuss,30
124 | Undergrad Data Analysis/Science internships SF Bay?,3 points by tctctc 15 days ago  | 5 comments,15
125 | The Role of Statistical Significance in Growth Hacking,6 points by rawls234 27 days ago  | discuss,27
126 | Data Science Course @ Harvard,7 points by rahmaniacc 29 days ago  | 2 comments,29
127 | Principal Component Projection Without Principal Component Analysis,6 points by genofon 27 days ago  | discuss,27
128 | "Machine Learning: An In-Depth, Non-Technical Guide - Part 3",7 points by innoarchitech 29 days ago  | discuss,29
129 | Stochastic Dummy Boosting,2 points by mikeskim 18 days ago  | discuss,18
130 | Interactive Map: Hong-Kong through The Lense of Instagram,2 points by BrianN 19 days ago  | discuss,19
131 | Data Science at Monsanto,3 points by doctorcroc 22 days ago  | discuss,22
132 | Data Science at Instacart,11 points by jeremystan 34 days ago  | 3 comments,34
133 | Building a Streaming Search Platform,6 points by ddrum001 28 days ago  | discuss,28
134 | Kafka Producer Latency with Large Topic Counts,3 points by marklit 26 days ago  | discuss,26
135 | A Sneak Peak of the Cloud: the 2 Minute Intro for Beginners,2 points by andymaheshw 20 days ago  | discuss,20
136 | Win-Vector video courses: price/status changes,2 points by jmount 20 days ago  | discuss,20
137 | 50+ Data Science and Machine Learning Cheat Sheets,20 points by elyase 42 days ago  | 1 comment,42
138 | One More Reason Not To Be Scared of Deep Learning,2 points by amplifier_khan 21 days ago  | discuss,21
139 | Visual Logic Authoring vs Code,2 points by AnnaOnTheWeb 21 days ago  | discuss,21
140 | Data Science in Python online training with hands-on experience,2 points by Puneet 21 days ago  | discuss,21
141 | Viewing the US Presidential Primary Through the Lens of Twitter,8 points by michelangelo 33 days ago  | discuss,33
142 | Caffe on Spark open sourced,4 points by rahmaniacc 27 days ago  | discuss,27
143 | The Ethical Data Scientist,5 points by tfturing 29 days ago  | discuss,29
144 | Answers to Frequently Asked Questions in Machine Learning,3 points by rasbt 21 days ago  | discuss,21
145 | Intro to A/B Testing and P-Values,2 points by randyzwitch 22 days ago  | discuss,22
146 | Visualizing State Level Data With R and Statebins,2 points by usujason 22 days ago  | discuss,22
147 | "Probabilistic Graphical Models slides & video lectures (Eric Xing, CMU)",4 points by ororm 28 days ago  | discuss,28
148 | Sense2vec with spaCy and Gensim,9 points by elyase 36 days ago  | 2 comments,36
149 | A Billion NYC Taxi and Uber Rides in AWS Redshift,3 points by marklit 31 days ago  | discuss,31
150 | How to Code and Understand DeepMind's Neural Stack Machine (in Python),2 points by genofon 23 days ago  | discuss,23
151 | How to make polished Jupyter presentations with optional code visibility,9 points by csaid81 36 days ago  | discuss,36
152 | How to become a Bayesian in eight easy steps,17 points by EtzA 44 days ago  | 1 comment,44
153 | Optimizing .*:  Details of Vectorization and Metaprogramming in Julia,4 points by randyzwitch 29 days ago  | discuss,29
154 | IBM certified Apache Spark Online Training,8 points by divya_jain 36 days ago  | discuss,36
155 | Geographic Data Science course,2 points by rk 25 days ago  | discuss,25
156 | "The Daily Mail Stole My Visualization, Twice",5 points by thehoff 32 days ago  | 1 comment,32
157 | Ensemble Methods: Improved Machine Learning Results,9 points by PyBloggers 38 days ago  | discuss,38
158 | Apache Spark and unsupervised learning in security,2 points by gradientflow 26 days ago  | discuss,26
159 | MachineJS: Automated machine learning- just give it a data file!,2 points by dsernst 26 days ago  | discuss,26
160 | The NSA?s SKYNET program may be killing thousands of innocent people,6 points by zlipp 35 days ago  | discuss,35
161 | "Big Dimensions, and What You Can Do About It",2 points by ramsey 27 days ago  | discuss,27
162 | Automate Your Oscars Pool with R,2 points by jamesdreiss 27 days ago  | discuss,27
163 | Signal Processing with LIGO GW150914 data,9 points by tfturing 39 days ago  | discuss,39
164 | Overview of DeZyre and Coursera Data Science Course,5 points by ann928 34 days ago  | discuss,34
165 | Upcoming Datathon in NYC,2 points by VicTrey 28 days ago  | discuss,28
166 | Summarizing Data in SQL,15 points by elisebreda 46 days ago  | discuss,46
167 | A/B Testing for Scammers,2 points by sameermanek 28 days ago  | discuss,28
168 | Highly interpretable classifiers for scikit learn using Bayesian decision rules,2 points by mcnulty 28 days ago  | discuss,28
169 | Auto-scaling scikit-learn with Spark,11 points by falaki 43 days ago  | discuss,43
170 | Where the f*** can I park?,2 points by manugarri 29 days ago  | discuss,29
171 | "Machine Learning: An In-Depth, Non-Technical Guide - Part 2",5 points by innoarchitech 36 days ago  | discuss,36
172 | Webhose.io now offers a historical data archive,7 points by databuffer 40 days ago  | discuss,40
173 | Meetup: Introduction to Machine Learning Algorithms for Data Science.,4 points by ann928 36 days ago  | discuss,36
174 | Exploring the Limits of Language Modeling,8 points by soates 42 days ago  | discuss,42
175 | Text Mining South Park,7 points by pmigdal 41 days ago  | discuss,41
176 | Finding the K in K-means by Parametric Bootstrap,7 points by jmount 42 days ago  | 1 comment,42
177 | Getting Started with Statistics for Data Science,3 points by nickhould 35 days ago  | discuss,35
178 | Rodeo 1.3 - Tab-completion for docstrings,3 points by glamp 35 days ago  | discuss,35
179 | Teaching D3.js - links,3 points by pmigdal 35 days ago  | discuss,35
180 | Parallel scikit-learn on YARN,5 points by stijntonk 39 days ago  | discuss,39
181 | Meetup: Free Live Webinar on Prescriptive Analytics for Fun and Profit,2 points by ann928 32 days ago  | discuss,32
182 | 


--------------------------------------------------------------------------------
/data/data_tau_ta.csv:
--------------------------------------------------------------------------------
  1 | title,date,days,tokens,stem,lemma,pos_tags,named_entities
  2 | Deep Advances in Generative Modeling,6 points by gwulfs 5 hours ago  | discuss,1,"deep,advances,generative,modeling",Deep Advances in Generative Model,Deep Advances in Generative Modeling,"[('Deep', 'JJ'), ('Advances', 'NNS'), ('in', 'IN'), ('Generative', 'NNP'), ('Modeling', 'NNP')]",['Generative Modeling']
  3 | A Neural Network in 11 lines of Python ,2 points by dekhtiar 5 hours ago  | discuss,1,"neural,network,11,lines,python",A Neural Network in 11 lines of Python ,A Neural Network in 11 lines of Python ,"[('A', 'DT'), ('Neural', 'NNP'), ('Network', 'NNP'), ('in', 'IN'), ('11', 'CD'), ('lines', 'NNS'), ('of', 'IN'), ('Python', 'NNP')]",['Python']
  4 | "Python, Machine Learning, and Language Wars",3 points by pmigdal 7 hours ago  | discuss,1,"python,machine,learning,language,wars","Python, Machine Learning, and Language War","Python, Machine Learning, and Language Wars","[('Python', 'NNP'), (',', ','), ('Machine', 'NNP'), ('Learning', 'NNP'), (',', ','), ('and', 'CC'), ('Language', 'NNP'), ('Wars', 'NNP')]","['Python', 'Machine Learning', 'Language Wars']"
  5 | Markov Chains Explained Visually,11 points by zeroviscosity 1 day ago  | 1 comment,1,"markov,chains,explained,visually",Markov Chains Explained Visu,Markov Chains Explained Visually,"[('Markov', 'NNP'), ('Chains', 'NNP'), ('Explained', 'VBD'), ('Visually', 'NNP')]","['Markov Chains', 'Visually']"
  6 | Dplython: Dplyr for Python,10 points by thenaturalist 1 day ago  | 3 comments,1,"dplython,dplyr,python",Dplython: Dplyr for Python,Dplython: Dplyr for Python,"[('Dplython', 'NN'), (':', ':'), ('Dplyr', 'NNP'), ('for', 'IN'), ('Python', 'NNP')]","['Dplython', 'Python']"
  7 | Inferring causal impact using Bayesian structural time-series models,7 points by Homunculiheaded 1 day ago  | 1 comment,1,"inferring,causal,impact,using,bayesian,structural,time,series,models",Inferring causal impact using Bayesian structural time-series model,Inferring causal impact using Bayesian structural time-series models,"[('Inferring', 'VBG'), ('causal', 'JJ'), ('impact', 'NN'), ('using', 'VBG'), ('Bayesian', 'JJ'), ('structural', 'JJ'), ('time', 'NN'), ('-', ':'), ('series', 'NN'), ('models', 'NNS')]",['Bayesian']
  8 | Tutorial: Web scraping and mapping breweries with import.io and R,4 points by jasdumas 1 day ago  | discuss,1,"tutorial,web,scraping,mapping,breweries,import,io,r",Tutorial: Web scraping and mapping breweries with import.io and R,Tutorial: Web scraping and mapping breweries with import.io and R,"[('Tutorial', 'JJ'), (':', ':'), ('Web', 'JJ'), ('scraping', 'NN'), ('and', 'CC'), ('mapping', 'NN'), ('breweries', 'NNS'), ('with', 'IN'), ('import', 'NN'), ('.', '.'), ('io', 'NN'), ('and', 'CC'), ('R', 'NN')]",[]
  9 | A Billion Taxi Rides on Amazon EMR running Spark,3 points by marklit 1 day ago  | 1 comment,1,"billion,taxi,rides,amazon,emr,running,spark",A Billion Taxi Rides on Amazon EMR running Spark,A Billion Taxi Rides on Amazon EMR running Spark,"[('A', 'DT'), ('Billion', 'NNP'), ('Taxi', 'NNP'), ('Rides', 'NNP'), ('on', 'IN'), ('Amazon', 'NNP'), ('EMR', 'NNP'), ('running', 'VBG'), ('Spark', 'NNP')]","['Amazon', 'Spark']"
 10 | The rise of greedy robots,4 points by yanir 1 day ago  | discuss,1,"rise,greedy,robots",The rise of greedy robot,The rise of greedy robots,"[('The', 'DT'), ('rise', 'NN'), ('of', 'IN'), ('greedy', 'NN'), ('robots', 'NNS')]",[]
 11 | Extracting image metadata at scale,2 points by zachwill 1 day ago  | discuss,1,"extracting,image,metadata,scale",Extracting image metadata at scal,Extracting image metadata at scale,"[('Extracting', 'VBG'), ('image', 'NN'), ('metadata', 'NN'), ('at', 'IN'), ('scale', 'NN')]",[]
 12 | "Python for Data Structures, Algorithms, and Interviews",17 points by kokoubaby 4 days ago  | discuss,4,"python,data,structures,algorithms,interviews","Python for Data Structures, Algorithms, and Interview","Python for Data Structures, Algorithms, and Interviews","[('Python', 'NNP'), ('for', 'IN'), ('Data', 'NNP'), ('Structures', 'NNP'), (',', ','), ('Algorithms', 'NNP'), (',', ','), ('and', 'CC'), ('Interviews', 'NNS')]","['Python', 'Data Structures', 'Algorithms']"
 13 | Lift charts - A data scientist's secret weapon,14 points by datenheini 4 days ago  | 2 comments,4,"lift,charts,data,scientist,secret,weapon",Lift charts - A data scientist's secret weapon,Lift charts - A data scientist's secret weapon,"[('Lift', 'NNP'), ('charts', 'VBZ'), ('-', ':'), ('A', 'DT'), ('data', 'JJ'), ('scientist', 'NN'), (""'"", 'POS'), ('s', 'NN'), ('secret', 'VBZ'), ('weapon', 'NN')]",['Lift']
 14 | How To Become A Machine Learning Expert In One Simple Step,4 points by swanint 2 days ago  | discuss,2,"become,machine,learning,expert,one,simple,step",How To Become A Machine Learning Expert In One Simple Step,How To Become A Machine Learning Expert In One Simple Step,"[('How', 'WRB'), ('To', 'TO'), ('Become', 'VB'), ('A', 'NNP'), ('Machine', 'NNP'), ('Learning', 'NNP'), ('Expert', 'NNP'), ('In', 'IN'), ('One', 'CD'), ('Simple', 'JJ'), ('Step', 'NN')]",[]
 15 | Data Science Side Project,6 points by yashpatel5400 1 day ago  | 8 comments,1,"data,science,side,project",Data Science Side Project,Data Science Side Project,"[('Data', 'NNP'), ('Science', 'NNP'), ('Side', 'NNP'), ('Project', 'NNP')]",['Data Science Side']
 16 | Simple estimation of hierarchical events with petersburg,3 points by wdm0006 1 day ago  | discuss,1,"simple,estimation,hierarchical,events,petersburg",Simple estimation of hierarchical events with petersburg,Simple estimation of hierarchical events with petersburg,"[('Simple', 'JJ'), ('estimation', 'NN'), ('of', 'IN'), ('hierarchical', 'JJ'), ('events', 'NNS'), ('with', 'IN'), ('petersburg', 'NN')]",['Simple']
 17 | Engineers Shouldn?t Write ETL: High Functioning Data Science Departments,9 points by legel 4 days ago  | 3 comments,4,"engineers,write,etl,high,functioning,data,science,departments",Engineers Shouldn?t Write ETL: High Functioning Data Science Depart,Engineers Shouldn?t Write ETL: High Functioning Data Science Departments,"[('Engineers', 'NNS'), ('Shouldn', 'NNP'), ('?', '.'), ('t', 'NN'), ('Write', 'NNP'), ('ETL', 'NNP'), (':', ':'), ('High', 'JJ'), ('Functioning', 'NNP'), ('Data', 'NNP'), ('Science', 'NNP'), ('Departments', 'NNP')]",['Write']
 18 | Unsupervised Computer Vision: The Current State of the Art,6 points by carlosfaham 3 days ago  | discuss,3,"unsupervised,computer,vision,current,state,art",Unsupervised Computer Vision: The Current State of the Art,Unsupervised Computer Vision: The Current State of the Art,"[('Unsupervised', 'VBN'), ('Computer', 'NNP'), ('Vision', 'NNP'), (':', ':'), ('The', 'DT'), ('Current', 'NNP'), ('State', 'NNP'), ('of', 'IN'), ('the', 'DT'), ('Art', 'NN')]",['Computer Vision']
 19 | What data visualization tools do /r/DataIsBeautiful OC creators use?,3 points by pmigdal 2 days ago  | discuss,2,"data,visualization,tools,r,dataisbeautiful,oc,creators,use",What data visualization tools do /r/DataIsBeautiful OC creators use?,What data visualization tools do /r/DataIsBeautiful OC creators use?,"[('What', 'WP'), ('data', 'VBZ'), ('visualization', 'NN'), ('tools', 'NNS'), ('do', 'VBP'), ('/', 'RB'), ('r', 'VB'), ('/', 'NNP'), ('DataIsBeautiful', 'NNP'), ('OC', 'NNP'), ('creators', 'NNS'), ('use', 'VBP'), ('?', '.')]",[]
 20 | Data Engineering at Slack: Twelve Mistakes I've Made In My First Three Months,13 points by gwulfs 5 days ago  | 2 comments,5,"data,engineering,slack,twelve,mistakes,made,first,three,months",Data Engineering at Slack: Twelve Mistakes I've Made In My First Three Month,Data Engineering at Slack: Twelve Mistakes I've Made In My First Three Months,"[('Data', 'NNP'), ('Engineering', 'NNP'), ('at', 'IN'), ('Slack', 'NNP'), (':', ':'), ('Twelve', 'NNP'), ('Mistakes', 'NNP'), ('I', 'PRP'), (""'"", ""''""), ('ve', 'NN'), ('Made', 'VBN'), ('In', 'IN'), ('My', 'NNP'), ('First', 'NNP'), ('Three', 'CD'), ('Months', 'NNP')]","['Data Engineering', 'Slack']"
 21 | An unusual interactive machine learning challenge,4 points by gglumov 3 days ago  | discuss,3,"unusual,interactive,machine,learning,challenge",An unusual interactive machine learning challeng,An unusual interactive machine learning challenge,"[('An', 'DT'), ('unusual', 'JJ'), ('interactive', 'JJ'), ('machine', 'NN'), ('learning', 'NN'), ('challenge', 'NN')]",[]
 22 | Datumbox Machine Learning Framework 0.7.0 Released,4 points by datumbox 3 days ago  | discuss,3,"datumbox,machine,learning,framework,0,7,0,released",Datumbox Machine Learning Framework 0.7.0 Releas,Datumbox Machine Learning Framework 0.7.0 Released,"[('Datumbox', 'NNP'), ('Machine', 'NNP'), ('Learning', 'NNP'), ('Framework', 'NNP'), ('0', 'CD'), ('.', '.'), ('7', 'CD'), ('.', '.'), ('0', 'CD'), ('Released', 'VBD')]",['Datumbox Machine']
 23 | Reshaping in Pandas,5 points by carlosgg 3 days ago  | discuss,3,"reshaping,pandas",Reshaping in Panda,Reshaping in Pandas,"[('Reshaping', 'VBG'), ('in', 'IN'), ('Pandas', 'NNP')]",['Pandas']
 24 | Data science intro for math/phys background,14 points by pmigdal 6 days ago  | discuss,6,"data,science,intro,math,phys,background",Data science intro for math/phys background,Data science intro for math/phys background,"[('Data', 'NNP'), ('science', 'NN'), ('intro', 'NN'), ('for', 'IN'), ('math', 'NN'), ('/', 'NNP'), ('phys', 'NN'), ('background', 'NN')]",['Data']
 25 | Neural Networks demystified,16 points by elyase 7 days ago  | discuss,7,"neural,networks,demystified",Neural Networks demystifi,Neural Networks demystified,"[('Neural', 'JJ'), ('Networks', 'NNP'), ('demystified', 'VBD')]",['Neural Networks']
 26 | What machines can learn from Apple Watch: detecting undiagnosed heart condition,9 points by koukouhappy 5 days ago  | discuss,5,"machines,learn,apple,watch,detecting,undiagnosed,heart,condition",What machines can learn from Apple Watch: detecting undiagnosed heart condit,What machines can learn from Apple Watch: detecting undiagnosed heart condition,"[('What', 'WP'), ('machines', 'NNS'), ('can', 'MD'), ('learn', 'VB'), ('from', 'IN'), ('Apple', 'NNP'), ('Watch', 'NNP'), (':', ':'), ('detecting', 'NN'), ('undiagnosed', 'JJ'), ('heart', 'NN'), ('condition', 'NN')]",['Apple Watch']
 27 | Data Science Tools: The Biggest Winners and Losers,12 points by AnnaOnTheWeb 7 days ago  | discuss,7,"data,science,tools,biggest,winners,losers",Data Science Tools: The Biggest Winners and Los,Data Science Tools: The Biggest Winners and Losers,"[('Data', 'NNP'), ('Science', 'NNP'), ('Tools', 'NNP'), (':', ':'), ('The', 'DT'), ('Biggest', 'NNP'), ('Winners', 'NNPS'), ('and', 'CC'), ('Losers', 'NNS')]","['Data Science Tools', 'Biggest Winners']"
 28 | 10 Years of Open Source Machine Learning,9 points by tstonez 6 days ago  | 1 comment,6,"10,years,open,source,machine,learning",10 Years of Open Source Machine Learn,10 Years of Open Source Machine Learning,"[('10', 'CD'), ('Years', 'NNS'), ('of', 'IN'), ('Open', 'NNP'), ('Source', 'NNP'), ('Machine', 'NNP'), ('Learning', 'NNP')]",['Open Source Machine']
 29 | Do jobs run in families?,5 points by Anon84 5 days ago  | 1 comment,5,"jobs,run,families",Do jobs run in families?,Do jobs run in families?,"[('Do', 'NNP'), ('jobs', 'NNS'), ('run', 'VB'), ('in', 'IN'), ('families', 'NNS'), ('?', '.')]",[]
 30 | Has your conversion rate changed? Bayesian timeseries analysis with Python,12 points by yummyfajitas 8 days ago  | discuss,8,"conversion,rate,changed,bayesian,timeseries,analysis,python",Has your conversion rate changed? Bayesian timeseries analysis with Python,Has your conversion rate changed? Bayesian timeseries analysis with Python,"[('Has', 'NNP'), ('your', 'PRP$'), ('conversion', 'NN'), ('rate', 'NN'), ('changed', 'VBN'), ('?', '.'), ('Bayesian', 'JJ'), ('timeseries', 'NNS'), ('analysis', 'NN'), ('with', 'IN'), ('Python', 'NNP')]",['Python']
 31 | "XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow",8 points by crowwork 7 days ago  | discuss,7,"xgboost4j,portable,distributed,xgboost,spark,flink,dataflow","XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow","XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow","[('XGBoost4J', 'NN'), (':', ':'), ('Portable', 'JJ'), ('Distributed', 'NNP'), ('XGboost', 'NN'), ('in', 'IN'), ('Spark', 'NNP'), (',', ','), ('Flink', 'NNP'), ('and', 'CC'), ('Dataflow', 'NNP')]","['XGBoost4J', 'Spark', 'Flink', 'Dataflow']"
 32 | Introduction to Scikit Flow -  Simplified Interface to TensorFlow,7 points by lefish 7 days ago  | discuss,7,"introduction,scikit,flow,simplified,interface,tensorflow",Introduction to Scikit Flow -  Simplified Interface to TensorFlow,Introduction to Scikit Flow -  Simplified Interface to TensorFlow,"[('Introduction', 'NN'), ('to', 'TO'), ('Scikit', 'NNP'), ('Flow', 'NNP'), ('-', ':'), ('Simplified', 'VBD'), ('Interface', 'NNP'), ('to', 'TO'), ('TensorFlow', 'VB')]",['Scikit Flow']
 33 | How to learn machine learning?,8 points by kiechu 7 days ago  | 1 comment,7,"learn,machine,learning",How to learn machine learning?,How to learn machine learning?,"[('How', 'WRB'), ('to', 'TO'), ('learn', 'VB'), ('machine', 'NN'), ('learning', 'NN'), ('?', '.')]",[]
 34 | The Deep Roots of Javascript Fatigue,5 points by nikkielizdemere 6 days ago  | 1 comment,6,"deep,roots,javascript,fatigue",The Deep Roots of Javascript Fatigu,The Deep Roots of Javascript Fatigue,"[('The', 'DT'), ('Deep', 'NNP'), ('Roots', 'NNP'), ('of', 'IN'), ('Javascript', 'NNP'), ('Fatigue', 'NNP')]","['Deep Roots', 'Javascript Fatigue']"
 35 | How do we make Data Tau work?,27 points by hal8 8 days ago  | 18 comments,8,"make,data,tau,work",How do we make Data Tau work?,How do we make Data Tau work?,"[('How', 'WRB'), ('do', 'VBP'), ('we', 'PRP'), ('make', 'VB'), ('Data', 'NNP'), ('Tau', 'NNP'), ('work', 'NN'), ('?', '.')]",['Data Tau']
 36 | "Machine Learning: An In-Depth, Non-Technical Guide???Part 4",7 points by innoarchitech 8 days ago  | discuss,8,"machine,learning,depth,non,technical,guide,???,part,4","Machine Learning: An In-Depth, Non-Technical Guide???Part 4","Machine Learning: An In-Depth, Non-Technical Guide???Part 4","[('Machine', 'NN'), ('Learning', 'NNP'), (':', ':'), ('An', 'DT'), ('In', 'IN'), ('-', ':'), ('Depth', 'NN'), (',', ','), ('Non', 'NNP'), ('-', ':'), ('Technical', 'NNP'), ('Guide', 'NNP'), ('???', 'NNP'), ('Part', 'NNP'), ('4', 'CD')]","['Machine Learning', 'Non', 'Technical Guide']"
 37 | Data Science Slack channel - Click for invite,7 points by jyotsna 8 days ago  | discuss,8,"data,science,slack,channel,click,invite",Data Science Slack channel - Click for invit,Data Science Slack channel - Click for invite,"[('Data', 'NNP'), ('Science', 'NNP'), ('Slack', 'NNP'), ('channel', 'NN'), ('-', ':'), ('Click', 'NN'), ('for', 'IN'), ('invite', 'NN')]","['Data Science Slack', 'Click']"
 38 | Genomic Data Visualization using Python,2 points by RadhouaneAniba 3 days ago  | discuss,3,"genomic,data,visualization,using,python",Genomic Data Visualization using Python,Genomic Data Visualization using Python,"[('Genomic', 'NNP'), ('Data', 'NNP'), ('Visualization', 'NNP'), ('using', 'VBG'), ('Python', 'NNP')]","['Genomic Data', 'Python']"
 39 | Descriptive Statistics in SQL,5 points by nickhould 7 days ago  | discuss,7,"descriptive,statistics,sql",Descriptive Statistics in SQL,Descriptive Statistics in SQL,"[('Descriptive', 'JJ'), ('Statistics', 'NNS'), ('in', 'IN'), ('SQL', 'NNP')]",['SQL']
 40 | "Playing ""Moneyball"" on EA FIFA 16",16 points by aabb13 13 days ago  | 3 comments,13,"playing,moneyball,ea,fifa,16","Playing ""Moneyball"" on EA FIFA 16","Playing ""Moneyball"" on EA FIFA 16","[('Playing', 'VBG'), ('""', 'NNP'), ('Moneyball', 'NNP'), ('""', 'NNP'), ('on', 'IN'), ('EA', 'NNP'), ('FIFA', 'NNP'), ('16', 'CD')]",[]
 41 | Intellexer - Natural Language Processing and Text Mining REST API,16 points by j_downer 13 days ago  | discuss,13,"intellexer,natural,language,processing,text,mining,rest,api",Intellexer - Natural Language Processing and Text Mining REST API,Intellexer - Natural Language Processing and Text Mining REST API,"[('Intellexer', 'NNP'), ('-', ':'), ('Natural', 'JJ'), ('Language', 'NNP'), ('Processing', 'NNP'), ('and', 'CC'), ('Text', 'NNP'), ('Mining', 'NNP'), ('REST', 'NNP'), ('API', 'NNP')]","['Intellexer', 'Natural Language', 'Text Mining']"
 42 | How to Use Cohort Data to Analyze User Behavior,2 points by clevertap 3 days ago  | discuss,3,"use,cohort,data,analyze,user,behavior",How to Use Cohort Data to Analyze User Behavior,How to Use Cohort Data to Analyze User Behavior,"[('How', 'WRB'), ('to', 'TO'), ('Use', 'VB'), ('Cohort', 'NNP'), ('Data', 'NNP'), ('to', 'TO'), ('Analyze', 'NNP'), ('User', 'NNP'), ('Behavior', 'NNP')]","['Cohort Data', 'Analyze User Behavior']"
 43 | Show DT: Datasets.co - An easy way to share and discover ml datasets,2 points by mrborgen86 4 days ago  | discuss,4,"show,dt,datasets,co,easy,way,share,discover,ml,datasets",Show DT: Datasets.co - An easy way to share and discover ml dataset,Show DT: Datasets.co - An easy way to share and discover ml datasets,"[('Show', 'NNP'), ('DT', 'NNP'), (':', ':'), ('Datasets', 'NNS'), ('.', '.'), ('co', 'SYM'), ('-', ':'), ('An', 'DT'), ('easy', 'JJ'), ('way', 'NN'), ('to', 'TO'), ('share', 'NN'), ('and', 'CC'), ('discover', 'NN'), ('ml', 'NN'), ('datasets', 'NNS')]",['Show']
 44 | "An Ode To The Rice Cooker, The Smartest Kitchen Appliance I?ve Ever Owned",2 points by tfturing 4 days ago  | discuss,4,"ode,rice,cooker,smartest,kitchen,appliance,ever,owned","An Ode To The Rice Cooker, The Smartest Kitchen Appliance I?ve Ever Own","An Ode To The Rice Cooker, The Smartest Kitchen Appliance I?ve Ever Owned","[('An', 'DT'), ('Ode', 'NNP'), ('To', 'TO'), ('The', 'DT'), ('Rice', 'NNP'), ('Cooker', 'NNP'), (',', ','), ('The', 'DT'), ('Smartest', 'NNP'), ('Kitchen', 'NNP'), ('Appliance', 'NNP'), ('I', 'PRP'), ('?', '.'), ('ve', ""''""), ('Ever', 'RB'), ('Owned', 'VBD')]","['Rice Cooker', 'Smartest Kitchen']"
 45 | Making transparent how variations in analytical choices affect results,4 points by rahmaniacc 6 days ago  | discuss,6,"making,transparent,variations,analytical,choices,affect,results",Making transparent how variations in analytical choices affect result,Making transparent how variations in analytical choices affect results,"[('Making', 'VBG'), ('transparent', 'JJ'), ('how', 'WRB'), ('variations', 'NNS'), ('in', 'IN'), ('analytical', 'JJ'), ('choices', 'NNS'), ('affect', 'VBP'), ('results', 'NNS')]",[]
 46 | [Ask DT] What are some rookie mistakes in R?,2 points by HKtemp 2 days ago  | discuss,2,"ask,dt,rookie,mistakes,r",[Ask DT] What are some rookie mistakes in R?,[Ask DT] What are some rookie mistakes in R?,"[('[', 'JJ'), ('Ask', 'NNP'), ('DT', 'NNP'), (']', 'NNP'), ('What', 'WP'), ('are', 'VBP'), ('some', 'DT'), ('rookie', 'NN'), ('mistakes', 'NNS'), ('in', 'IN'), ('R', 'NNP'), ('?', '.')]",[]
 47 | Is Scala a better choice than Python for Apache Spark?,6 points by srinify 9 days ago  | 1 comment,9,"scala,better,choice,python,apache,spark",Is Scala a better choice than Python for Apache Spark?,Is Scala a better choice than Python for Apache Spark?,"[('Is', 'VBZ'), ('Scala', 'NNP'), ('a', 'DT'), ('better', 'JJR'), ('choice', 'NN'), ('than', 'IN'), ('Python', 'NNP'), ('for', 'IN'), ('Apache', 'NNP'), ('Spark', 'NNP'), ('?', '.')]","['Python', 'Apache Spark']"
 48 | Julia: A Fast Language for Numerical Computing,6 points by srinify 9 days ago  | 1 comment,9,"julia,fast,language,numerical,computing",Julia: A Fast Language for Numerical Comput,Julia: A Fast Language for Numerical Computing,"[('Julia', 'NNS'), (':', ':'), ('A', 'DT'), ('Fast', 'NNP'), ('Language', 'NNP'), ('for', 'IN'), ('Numerical', 'NNP'), ('Computing', 'NNP')]",['Numerical Computing']
 49 | Analyzing Golden State Warriors' passing network using GraphFrames in Spark,3 points by yukiegosapporo 6 days ago  | discuss,6,"analyzing,golden,state,warriors,passing,network,using,graphframes,spark",Analyzing Golden State Warriors' passing network using GraphFrames in Spark,Analyzing Golden State Warriors' passing network using GraphFrames in Spark,"[('Analyzing', 'VBG'), ('Golden', 'NNP'), ('State', 'NNP'), ('Warriors', 'NNP'), (""'"", 'POS'), ('passing', 'NN'), ('network', 'NN'), ('using', 'VBG'), ('GraphFrames', 'NNP'), ('in', 'IN'), ('Spark', 'NNP')]","['Golden State Warriors', 'GraphFrames', 'Spark']"
 50 | Megaman: Manifold Learning with Millions of points,4 points by dperry 7 days ago  | 3 comments,7,"megaman,manifold,learning,millions,points",Megaman: Manifold Learning with Millions of point,Megaman: Manifold Learning with Millions of points,"[('Megaman', 'NN'), (':', ':'), ('Manifold', 'NNP'), ('Learning', 'VBG'), ('with', 'IN'), ('Millions', 'NNP'), ('of', 'IN'), ('points', 'NNS')]","['Megaman', 'Millions']"
 51 | How to Detect Outliers on Parametric and Non Parametric Methods,2 points by clevertap 4 days ago  | discuss,4,"detect,outliers,parametric,non,parametric,methods",How to Detect Outliers on Parametric and Non Parametric Method,How to Detect Outliers on Parametric and Non Parametric Methods,"[('How', 'WRB'), ('to', 'TO'), ('Detect', 'VB'), ('Outliers', 'NNP'), ('on', 'IN'), ('Parametric', 'NNP'), ('and', 'CC'), ('Non', 'NNP'), ('Parametric', 'NNP'), ('Methods', 'NNP')]","['Outliers', 'Parametric', 'Non Parametric Methods']"
 52 | BallR: Interactive NBA Shot Charts with R and Shiny,12 points by carlosgg 14 days ago  | discuss,14,"ballr,interactive,nba,shot,charts,r,shiny",BallR: Interactive NBA Shot Charts with R and Shini,BallR: Interactive NBA Shot Charts with R and Shiny,"[('BallR', 'NN'), (':', ':'), ('Interactive', 'JJ'), ('NBA', 'NNP'), ('Shot', 'NNP'), ('Charts', 'NNP'), ('with', 'IN'), ('R', 'NNP'), ('and', 'CC'), ('Shiny', 'NNP')]","['BallR', 'NBA Shot', 'Shiny']"
 53 | Minecraft to run artificial intelligence experiments,4 points by bsadeghi 8 days ago  | discuss,8,"minecraft,run,artificial,intelligence,experiments",Minecraft to run artificial intelligence experi,Minecraft to run artificial intelligence experiments,"[('Minecraft', 'NN'), ('to', 'TO'), ('run', 'VB'), ('artificial', 'JJ'), ('intelligence', 'NN'), ('experiments', 'NNS')]",['Minecraft']
 54 | Deep Q-Learning (Space Invaders),4 points by pmigdal 8 days ago  | discuss,8,"deep,q,learning,space,invaders",Deep Q-Learning (Space Invaders),Deep Q-Learning (Space Invaders),"[('Deep', 'NNP'), ('Q', 'NNP'), ('-', ':'), ('Learning', 'NNP'), ('(', '('), ('Space', 'NNP'), ('Invaders', 'NNP'), (')', ')')]","['Deep', 'Space Invaders']"
 55 | Theano Tutorial,2 points by pmigdal 5 days ago  | discuss,5,"theano,tutorial",Theano Tutori,Theano Tutorial,"[('Theano', 'NNP'), ('Tutorial', 'NNP')]",['Theano Tutorial']
 56 | Computing Classification Evaluation Metrics in R,3 points by lefish 7 days ago  | discuss,7,"computing,classification,evaluation,metrics,r",Computing Classification Evaluation Metrics in R,Computing Classification Evaluation Metrics in R,"[('Computing', 'VBG'), ('Classification', 'NNP'), ('Evaluation', 'NNP'), ('Metrics', 'NNP'), ('in', 'IN'), ('R', 'NNP')]",[]
 57 | The Personality Space of Cartoon Characters,3 points by lefish 7 days ago  | discuss,7,"personality,space,cartoon,characters",The Personality Space of Cartoon Charact,The Personality Space of Cartoon Characters,"[('The', 'DT'), ('Personality', 'NNP'), ('Space', 'NNP'), ('of', 'IN'), ('Cartoon', 'NNP'), ('Characters', 'NNS')]","['Personality Space', 'Cartoon']"
 58 | Announcing Apache Flink 1.0.0,11 points by mxm 14 days ago  | discuss,14,"announcing,apache,flink,1,0,0",Announcing Apache Flink 1.0.0,Announcing Apache Flink 1.0.0,"[('Announcing', 'VBG'), ('Apache', 'NNP'), ('Flink', 'NNP'), ('1', 'CD'), ('.', '.'), ('0', 'CD'), ('.', '.'), ('0', 'CD')]",['Apache']
 59 | Bayesian Reasoning in The Twilight Zone!,2 points by Homunculiheaded 5 days ago  | discuss,5,"bayesian,reasoning,twilight,zone",Bayesian Reasoning in The Twilight Zone!,Bayesian Reasoning in The Twilight Zone!,"[('Bayesian', 'JJ'), ('Reasoning', 'NNP'), ('in', 'IN'), ('The', 'DT'), ('Twilight', 'NNP'), ('Zone', 'NN'), ('!', '.')]","['Bayesian Reasoning', 'Twilight Zone']"
 60 | Bayesian Estimation of G Train Wait Times,7 points by jamesdreiss 12 days ago  | discuss,12,"bayesian,estimation,g,train,wait,times",Bayesian Estimation of G Train Wait Tim,Bayesian Estimation of G Train Wait Times,"[('Bayesian', 'JJ'), ('Estimation', 'NNP'), ('of', 'IN'), ('G', 'NNP'), ('Train', 'NNP'), ('Wait', 'NNP'), ('Times', 'NNP')]",['Bayesian Estimation']
 61 | Some experiments into explaining complex black box ensemble predictions,2 points by lefish 6 days ago  | discuss,6,"experiments,explaining,complex,black,box,ensemble,predictions",Some experiments into explaining complex black box ensemble predict,Some experiments into explaining complex black box ensemble predictions,"[('Some', 'DT'), ('experiments', 'NNS'), ('into', 'IN'), ('explaining', 'VBG'), ('complex', 'JJ'), ('black', 'JJ'), ('box', 'NN'), ('ensemble', 'JJ'), ('predictions', 'NNS')]",[]
 62 | Creating a Hadoop Pseudo-Distributed Environment,2 points by lefish 6 days ago  | discuss,6,"creating,hadoop,pseudo,distributed,environment",Creating a Hadoop Pseudo-Distributed Environ,Creating a Hadoop Pseudo-Distributed Environment,"[('Creating', 'VBG'), ('a', 'DT'), ('Hadoop', 'NNP'), ('Pseudo', 'NNP'), ('-', ':'), ('Distributed', 'VBD'), ('Environment', 'JJ')]",['Hadoop Pseudo']
 63 | "Data Science Pop-Up in Austin, TX",2 points by AnnaOnTheWeb 6 days ago  | discuss,6,"data,science,pop,austin,tx","Data Science Pop-Up in Austin, TX","Data Science Pop-Up in Austin, TX","[('Data', 'NNP'), ('Science', 'NNP'), ('Pop', 'NNP'), ('-', ':'), ('Up', 'NN'), ('in', 'IN'), ('Austin', 'NNP'), (',', ','), ('TX', 'NNP')]","['Data Science Pop', 'Austin']"
 64 | A Billion Taxi Rides on Amazon EMR Running Presto,3 points by marklit 8 days ago  | discuss,8,"billion,taxi,rides,amazon,emr,running,presto",A Billion Taxi Rides on Amazon EMR Running Presto,A Billion Taxi Rides on Amazon EMR Running Presto,"[('A', 'DT'), ('Billion', 'NNP'), ('Taxi', 'NNP'), ('Rides', 'NNP'), ('on', 'IN'), ('Amazon', 'NNP'), ('EMR', 'NNP'), ('Running', 'NNP'), ('Presto', 'NNP')]",['Amazon']
 65 | Train your own image classifier with Inception in TensorFlow,7 points by elyase 12 days ago  | discuss,12,"train,image,classifier,inception,tensorflow",Train your own image classifier with Inception in TensorFlow,Train your own image classifier with Inception in TensorFlow,"[('Train', 'VB'), ('your', 'PRP$'), ('own', 'JJ'), ('image', 'NN'), ('classifier', 'NN'), ('with', 'IN'), ('Inception', 'NNP'), ('in', 'IN'), ('TensorFlow', 'NNP')]",['TensorFlow']
 66 | Statisticians Agree: It?s Time To Stop Misusing P-Value,9 points by jpiburn 15 days ago  | 5 comments,15,"statisticians,agree,time,stop,misusing,p,value",Statisticians Agree: It?s Time To Stop Misusing P-Valu,Statisticians Agree: It?s Time To Stop Misusing P-Value,"[('Statisticians', 'NNS'), ('Agree', 'VBP'), (':', ':'), ('It', 'PRP'), ('?', '.'), ('s', 'JJ'), ('Time', 'NNP'), ('To', 'TO'), ('Stop', 'VB'), ('Misusing', 'NNP'), ('P', 'NNP'), ('-', ':'), ('Value', 'NN')]",[]
 67 | Shiny app for running a Tensorflow demo,3 points by shinyman 8 days ago  | discuss,8,"shiny,app,running,tensorflow,demo",Shiny app for running a Tensorflow demo,Shiny app for running a Tensorflow demo,"[('Shiny', 'NNP'), ('app', 'NN'), ('for', 'IN'), ('running', 'VBG'), ('a', 'DT'), ('Tensorflow', 'NNP'), ('demo', 'NN')]","['Shiny', 'Tensorflow']"
 68 | File details and owners with gitnoc and git-pandas,3 points by wdm0006 9 days ago  | discuss,9,"file,details,owners,gitnoc,git,pandas",File details and owners with gitnoc and git-panda,File details and owners with gitnoc and git-pandas,"[('File', 'NN'), ('details', 'NNS'), ('and', 'CC'), ('owners', 'NNS'), ('with', 'IN'), ('gitnoc', 'NN'), ('and', 'CC'), ('git', 'JJ'), ('-', ':'), ('pandas', 'NN')]",['File']
 69 | 7 Big Data Technologies and When to Use Them that All Data Engineers Should Know,2 points by galvanize 7 days ago  | discuss,7,"7,big,data,technologies,use,data,engineers,know",7 Big Data Technologies and When to Use Them that All Data Engineers Should Know,7 Big Data Technologies and When to Use Them that All Data Engineers Should Know,"[('7', 'CD'), ('Big', 'NNP'), ('Data', 'NNP'), ('Technologies', 'NNPS'), ('and', 'CC'), ('When', 'WRB'), ('to', 'TO'), ('Use', 'VB'), ('Them', 'NNP'), ('that', 'IN'), ('All', 'NNP'), ('Data', 'NNP'), ('Engineers', 'NNP'), ('Should', 'NNP'), ('Know', 'NNP')]",['All Data Engineers Should Know']
 70 | Topic clusters with TF-IDF vectorization with Spark and Scala,2 points by lefish 7 days ago  | discuss,7,"topic,clusters,tf,idf,vectorization,spark,scala",Topic clusters with TF-IDF vectorization with Spark and Scala,Topic clusters with TF-IDF vectorization with Spark and Scala,"[('Topic', 'NN'), ('clusters', 'NNS'), ('with', 'IN'), ('TF', 'NNP'), ('-', ':'), ('IDF', 'NNP'), ('vectorization', 'NN'), ('with', 'IN'), ('Spark', 'NNP'), ('and', 'CC'), ('Scala', 'NNP')]","['Topic', 'TF', 'IDF', 'Spark', 'Scala']"
 71 | Neural Doodles: Workflows for the Next Generation of Artists,5 points by pmigdal 12 days ago  | discuss,12,"neural,doodles,workflows,next,generation,artists",Neural Doodles: Workflows for the Next Generation of Artist,Neural Doodles: Workflows for the Next Generation of Artists,"[('Neural', 'JJ'), ('Doodles', 'NNS'), (':', ':'), ('Workflows', 'NNS'), ('for', 'IN'), ('the', 'DT'), ('Next', 'JJ'), ('Generation', 'NNP'), ('of', 'IN'), ('Artists', 'NNS')]",['Next Generation']
 72 | Graph Databases 101,5 points by carlosgg 12 days ago  | discuss,12,"graph,databases,101",Graph Databases 101,Graph Databases 101,"[('Graph', 'NNP'), ('Databases', 'VBZ'), ('101', 'CD')]",['Graph']
 73 | "Telemetry with Collectd, Logstash, Elasticsearch and Grafana (ELG)",2 points by helloanand 7 days ago  | discuss,7,"telemetry,collectd,logstash,elasticsearch,grafana,elg","Telemetry with Collectd, Logstash, Elasticsearch and Grafana (ELG)","Telemetry with Collectd, Logstash, Elasticsearch and Grafana (ELG)","[('Telemetry', 'NN'), ('with', 'IN'), ('Collectd', 'NNP'), (',', ','), ('Logstash', 'NNP'), (',', ','), ('Elasticsearch', 'NNP'), ('and', 'CC'), ('Grafana', 'NNP'), ('(', '('), ('ELG', 'NNP'), (')', ')')]","['Telemetry', 'Collectd', 'Logstash', 'Elasticsearch', 'Grafana', 'ELG']"
 74 | XGBoost: A Scalable Tree Boosting System article,5 points by tfturing 12 days ago  | discuss,12,"xgboost,scalable,tree,boosting,system,article",XGBoost: A Scalable Tree Boosting System articl,XGBoost: A Scalable Tree Boosting System article,"[('XGBoost', 'NN'), (':', ':'), ('A', 'DT'), ('Scalable', 'JJ'), ('Tree', 'NNP'), ('Boosting', 'NNP'), ('System', 'NNP'), ('article', 'NN')]",['XGBoost']
 75 | DataRadar.IO - Data Science RSS Feed - Do you have enough data about your data,2 points by dekhtiar 8 days ago  | 3 comments,8,"dataradar,io,data,science,rss,feed,enough,data,data",DataRadar.IO - Data Science RSS Feed - Do you have enough data about your data,DataRadar.IO - Data Science RSS Feed - Do you have enough data about your data,"[('DataRadar', 'NNP'), ('.', '.'), ('IO', 'NNP'), ('-', ':'), ('Data', 'NNP'), ('Science', 'NNP'), ('RSS', 'NNP'), ('Feed', 'NNP'), ('-', ':'), ('Do', 'VBP'), ('you', 'PRP'), ('have', 'VBP'), ('enough', 'VBN'), ('data', 'NNS'), ('about', 'IN'), ('your', 'PRP$'), ('data', 'NNS')]","['DataRadar', 'IO', 'Data Science']"
 76 | International Women's Day: What #PledgeForParity Means To Us,5 points by ddrum001 13 days ago  | discuss,13,"international,women,day,#,pledgeforparity,means,us",International Women's Day: What #PledgeForParity Means To U,International Women's Day: What #PledgeForParity Means To Us,"[('International', 'NNP'), ('Women', 'NNP'), (""'"", 'POS'), ('s', 'JJ'), ('Day', 'NNP'), (':', ':'), ('What', 'WP'), ('#', '#'), ('PledgeForParity', 'NN'), ('Means', 'NNPS'), ('To', 'TO'), ('Us', 'VB')]","['International Women', 'PledgeForParity Means']"
 77 | Top 50 Data Science thought leaders on Twitter,3 points by datawerq 11 days ago  | 3 comments,11,"top,50,data,science,thought,leaders,twitter",Top 50 Data Science thought leaders on Twitt,Top 50 Data Science thought leaders on Twitter,"[('Top', 'JJ'), ('50', 'CD'), ('Data', 'NNP'), ('Science', 'NNP'), ('thought', 'VBD'), ('leaders', 'NNS'), ('on', 'IN'), ('Twitter', 'NN')]",[]
 78 | Ask DT: Who Is Hiring? (March 2016),27 points by whoishiring 21 days ago  | 15 comments,21,"ask,dt,hiring,march,2016",Ask DT: Who Is Hiring? (March 2016),Ask DT: Who Is Hiring? (March 2016),"[('Ask', 'NNP'), ('DT', 'NNP'), (':', ':'), ('Who', 'WP'), ('Is', 'VBZ'), ('Hiring', 'VBG'), ('?', '.'), ('(', '('), ('March', 'NNP'), ('2016', 'CD'), (')', ')')]",['Ask']
 79 | Introducing GraphFrames,7 points by falaki 18 days ago  | discuss,18,"introducing,graphframes",Introducing GraphFram,Introducing GraphFrames,"[('Introducing', 'VBG'), ('GraphFrames', 'NNS')]",[]
 80 | Announcing R Tools for Visual Studio,3 points by brakmic 13 days ago  | discuss,13,"announcing,r,tools,visual,studio",Announcing R Tools for Visual Studio,Announcing R Tools for Visual Studio,"[('Announcing', 'VBG'), ('R', 'NNP'), ('Tools', 'NNP'), ('for', 'IN'), ('Visual', 'NNP'), ('Studio', 'NNP')]",['Visual Studio']
 81 | Question: What do you want to say about working with data?,2 points by emiller425 7 days ago  | discuss,7,"question,want,say,working,data",Question: What do you want to say about working with data?,Question: What do you want to say about working with data?,"[('Question', 'NN'), (':', ':'), ('What', 'WP'), ('do', 'VBP'), ('you', 'PRP'), ('want', 'VB'), ('to', 'TO'), ('say', 'VB'), ('about', 'IN'), ('working', 'VBG'), ('with', 'IN'), ('data', 'NNS'), ('?', '.')]",[]
 82 | Genomic Ranges - an Introduction to Working with Genomic Data,3 points by AnnaOnTheWeb 13 days ago  | discuss,13,"genomic,ranges,introduction,working,genomic,data",Genomic Ranges - an Introduction to Working with Genomic Data,Genomic Ranges - an Introduction to Working with Genomic Data,"[('Genomic', 'NNP'), ('Ranges', 'NNP'), ('-', ':'), ('an', 'DT'), ('Introduction', 'NN'), ('to', 'TO'), ('Working', 'VBG'), ('with', 'IN'), ('Genomic', 'NNP'), ('Data', 'NNP')]","['Genomic Ranges', 'Genomic Data']"
 83 | TensorFlow for Poets,9 points by ebellm 21 days ago  | 1 comment,21,"tensorflow,poets",TensorFlow for Poet,TensorFlow for Poets,"[('TensorFlow', 'NNP'), ('for', 'IN'), ('Poets', 'NNS')]",['TensorFlow']
 84 | Unsupervised Learning with Even Less Supervision Using Bayesian Optimization,2 points by idewanck 10 days ago  | discuss,10,"unsupervised,learning,even,less,supervision,using,bayesian,optimization",Unsupervised Learning with Even Less Supervision Using Bayesian Optim,Unsupervised Learning with Even Less Supervision Using Bayesian Optimization,"[('Unsupervised', 'VBN'), ('Learning', 'VBG'), ('with', 'IN'), ('Even', 'NNP'), ('Less', 'NNP'), ('Supervision', 'NNP'), ('Using', 'NNP'), ('Bayesian', 'NNP'), ('Optimization', 'NNP')]",['Even Less Supervision Using Bayesian']
 85 | How to work with large JSON datasets using Python and Pandas,9 points by brian_spiering 21 days ago  | discuss,21,"work,large,json,datasets,using,python,pandas",How to work with large JSON datasets using Python and Panda,How to work with large JSON datasets using Python and Pandas,"[('How', 'WRB'), ('to', 'TO'), ('work', 'VB'), ('with', 'IN'), ('large', 'JJ'), ('JSON', 'NNP'), ('datasets', 'NNS'), ('using', 'VBG'), ('Python', 'NNP'), ('and', 'CC'), ('Pandas', 'NNP')]","['JSON', 'Python', 'Pandas']"
 86 | DrivenData Competition: Model/Visualize Fog Patterns in Morocco,4 points by bull 15 days ago  | discuss,15,"drivendata,competition,model,visualize,fog,patterns,morocco",DrivenData Competition: Model/Visualize Fog Patterns in Morocco,DrivenData Competition: Model/Visualize Fog Patterns in Morocco,"[('DrivenData', 'NNP'), ('Competition', 'NN'), (':', ':'), ('Model', 'NNP'), ('/', 'NNP'), ('Visualize', 'NNP'), ('Fog', 'NNP'), ('Patterns', 'NNP'), ('in', 'IN'), ('Morocco', 'NNP')]","['DrivenData', 'Morocco']"
 87 | Deriving Better Insights From Time Series Data With Cycle Plots,2 points by clevertap 11 days ago  | discuss,11,"deriving,better,insights,time,series,data,cycle,plots",Deriving Better Insights From Time Series Data With Cycle Plot,Deriving Better Insights From Time Series Data With Cycle Plots,"[('Deriving', 'VBG'), ('Better', 'NNP'), ('Insights', 'NNPS'), ('From', 'NNP'), ('Time', 'NNP'), ('Series', 'NNP'), ('Data', 'NNP'), ('With', 'IN'), ('Cycle', 'NNP'), ('Plots', 'NNP')]","['Better Insights From Time Series Data', 'Cycle Plots']"
 88 | Deep Learning: Nine Lectures at Coll?ge de France by Yan LeCun,5 points by Anon84 16 days ago  | discuss,16,"deep,learning,nine,lectures,coll,ge,de,france,yan,lecun",Deep Learning: Nine Lectures at Coll?ge de France by Yan LeCun,Deep Learning: Nine Lectures at Coll?ge de France by Yan LeCun,"[('Deep', 'JJ'), ('Learning', 'NNP'), (':', ':'), ('Nine', 'JJ'), ('Lectures', 'NNS'), ('at', 'IN'), ('Coll', 'NNP'), ('?', '.'), ('ge', 'NN'), ('de', 'IN'), ('France', 'NNP'), ('by', 'IN'), ('Yan', 'NNP'), ('LeCun', 'NNP')]","['Deep', 'Coll', 'France', 'Yan']"
 89 | SQL for Data Analysis,3 points by nickhould 14 days ago  | 6 comments,14,"sql,data,analysis",SQL for Data Analysi,SQL for Data Analysis,"[('SQL', 'NNP'), ('for', 'IN'), ('Data', 'NNP'), ('Analysis', 'NNP')]","['SQL', 'Data Analysis']"
 90 | Stream processing and messaging systems for the IoT age,2 points by gradientflow 12 days ago  | discuss,12,"stream,processing,messaging,systems,iot,age",Stream processing and messaging systems for the IoT ag,Stream processing and messaging systems for the IoT age,"[('Stream', 'NN'), ('processing', 'NN'), ('and', 'CC'), ('messaging', 'VBG'), ('systems', 'NNS'), ('for', 'IN'), ('the', 'DT'), ('IoT', 'NNP'), ('age', 'NN')]","['Stream', 'IoT']"
 91 | Optimizing Facebook Campaigns with R,2 points by AnnaOnTheWeb 12 days ago  | 1 comment,12,"optimizing,facebook,campaigns,r",Optimizing Facebook Campaigns with R,Optimizing Facebook Campaigns with R,"[('Optimizing', 'VBG'), ('Facebook', 'NNP'), ('Campaigns', 'NNP'), ('with', 'IN'), ('R', 'NNP')]",['Facebook Campaigns']
 92 | "Trump Tweets on a Globe (aka Fun with d3, socket.io, and the Twitter API)",8 points by joelgrus 21 days ago  | discuss,21,"trump,tweets,globe,aka,fun,d3,socket,io,twitter,api","Trump Tweets on a Globe (aka Fun with d3, socket.io, and the Twitter API)","Trump Tweets on a Globe (aka Fun with d3, socket.io, and the Twitter API)","[('Trump', 'NNP'), ('Tweets', 'NNP'), ('on', 'IN'), ('a', 'DT'), ('Globe', 'NNP'), ('(', '('), ('aka', 'JJ'), ('Fun', 'NNP'), ('with', 'IN'), ('d3', 'NN'), (',', ','), ('socket', 'NN'), ('.', '.'), ('io', 'NN'), (',', ','), ('and', 'CC'), ('the', 'DT'), ('Twitter', 'NNP'), ('API', 'NNP'), (')', ')')]","['Trump Tweets', 'Twitter']"
 93 | Why pandas users should be excited about Apache Arrow,17 points by pmigdal 28 days ago  | discuss,28,"pandas,users,excited,apache,arrow",Why pandas users should be excited about Apache Arrow,Why pandas users should be excited about Apache Arrow,"[('Why', 'WRB'), ('pandas', 'JJ'), ('users', 'NNS'), ('should', 'MD'), ('be', 'VB'), ('excited', 'VBN'), ('about', 'IN'), ('Apache', 'NNP'), ('Arrow', 'NNP')]",['Apache Arrow']
 94 | Histogram intersection for change detection,8 points by datadive 22 days ago  | discuss,22,"histogram,intersection,change,detection",Histogram intersection for change detect,Histogram intersection for change detection,"[('Histogram', 'NNP'), ('intersection', 'NN'), ('for', 'IN'), ('change', 'NN'), ('detection', 'NN')]",['Histogram']
 95 | A simpler way to merge data streams,2 points by apoverton 13 days ago  | discuss,13,"simpler,way,merge,data,streams",A simpler way to merge data stream,A simpler way to merge data streams,"[('A', 'DT'), ('simpler', 'JJ'), ('way', 'NN'), ('to', 'TO'), ('merge', 'VB'), ('data', 'NNS'), ('streams', 'NNS')]",[]
 96 | Distributed TensorFlow just open-sourced,10 points by elyase 25 days ago  | discuss,25,"distributed,tensorflow,open,sourced",Distributed TensorFlow just open-sourc,Distributed TensorFlow just open-sourced,"[('Distributed', 'VBN'), ('TensorFlow', 'NNP'), ('just', 'RB'), ('open', 'VB'), ('-', ':'), ('sourced', 'VBN')]",['TensorFlow']
 97 | D3.js Screencasts (1 in 3 are free),4 points by Veerle 18 days ago  | discuss,18,"d3,js,screencasts,1,3,free",D3.js Screencasts (1 in 3 are free),D3.js Screencasts (1 in 3 are free),"[('D3', 'NNP'), ('.', '.'), ('js', 'NN'), ('Screencasts', 'NNS'), ('(', '('), ('1', 'CD'), ('in', 'IN'), ('3', 'CD'), ('are', 'VBP'), ('free', 'JJ'), (')', ')')]",[]
 98 | Regression and Classification with Examples in R,5 points by soates 19 days ago  | discuss,19,"regression,classification,examples,r",Regression and Classification with Examples in R,Regression and Classification with Examples in R,"[('Regression', 'NN'), ('and', 'CC'), ('Classification', 'NN'), ('with', 'IN'), ('Examples', 'NNP'), ('in', 'IN'), ('R', 'NNP')]","['Regression', 'Examples']"
 99 | Free online course on statistical shape modelling,8 points by shapemean 25 days ago  | discuss,25,"free,online,course,statistical,shape,modelling",Free online course on statistical shape model,Free online course on statistical shape modelling,"[('Free', 'JJ'), ('online', 'NN'), ('course', 'NN'), ('on', 'IN'), ('statistical', 'JJ'), ('shape', 'NN'), ('modelling', 'NN')]",['Free']
100 | "Don't worry about deep learning, deepen your understanding of causality instead",22 points by yanir 36 days ago  | discuss,36,"worry,deep,learning,deepen,understanding,causality,instead","Don't worry about deep learning, deepen your understanding of causality instead","Don't worry about deep learning, deepen your understanding of causality instead","[('Don', 'NNP'), (""'"", 'POS'), ('t', 'NN'), ('worry', 'VBP'), ('about', 'IN'), ('deep', 'JJ'), ('learning', 'NN'), (',', ','), ('deepen', 'VB'), ('your', 'PRP$'), ('understanding', 'NN'), ('of', 'IN'), ('causality', 'NN'), ('instead', 'RB')]",['Don']
101 | Skizze - A high throughput probabilistic data structure service and storage,2 points by seiflotfy 14 days ago  | discuss,14,"skizze,high,throughput,probabilistic,data,structure,service,storage",Skizze - A high throughput probabilistic data structure service and storag,Skizze - A high throughput probabilistic data structure service and storage,"[('Skizze', 'NNP'), ('-', ':'), ('A', 'DT'), ('high', 'JJ'), ('throughput', 'NN'), ('probabilistic', 'JJ'), ('data', 'NNS'), ('structure', 'NN'), ('service', 'NN'), ('and', 'CC'), ('storage', 'NN')]",['Skizze']
102 | Work with private repositories and other updates of the FlyElephant platform,2 points by m31 14 days ago  | discuss,14,"work,private,repositories,updates,flyelephant,platform",Work with private repositories and other updates of the FlyElephant platform,Work with private repositories and other updates of the FlyElephant platform,"[('Work', 'NN'), ('with', 'IN'), ('private', 'JJ'), ('repositories', 'NNS'), ('and', 'CC'), ('other', 'JJ'), ('updates', 'NNS'), ('of', 'IN'), ('the', 'DT'), ('FlyElephant', 'NNP'), ('platform', 'NN')]","['Work', 'FlyElephant']"
103 | How to import XML to almost anywhere,4 points by Jammink 19 days ago  | discuss,19,"import,xml,almost,anywhere",How to import XML to almost anywher,How to import XML to almost anywhere,"[('How', 'WRB'), ('to', 'TO'), ('import', 'VB'), ('XML', 'NN'), ('to', 'TO'), ('almost', 'RB'), ('anywhere', 'VB')]",['XML']
104 | Optimizing Notification Timing for One Signal,8 points by megandias 25 days ago  | discuss,25,"optimizing,notification,timing,one,signal",Optimizing Notification Timing for One Sign,Optimizing Notification Timing for One Signal,"[('Optimizing', 'VBG'), ('Notification', 'NNP'), ('Timing', 'NNP'), ('for', 'IN'), ('One', 'CD'), ('Signal', 'NNP')]",[]
105 | Survival Analysis of Cricket Player Careers,8 points by keshav92 25 days ago  | 6 comments,25,"survival,analysis,cricket,player,careers",Survival Analysis of Cricket Player Car,Survival Analysis of Cricket Player Careers,"[('Survival', 'JJ'), ('Analysis', 'NN'), ('of', 'IN'), ('Cricket', 'NNP'), ('Player', 'NNP'), ('Careers', 'NNP')]",['Cricket Player Careers']
106 | Generate image analogies using neural matching and blending,2 points by pmigdal 15 days ago  | discuss,15,"generate,image,analogies,using,neural,matching,blending",Generate image analogies using neural matching and blend,Generate image analogies using neural matching and blending,"[('Generate', 'NNP'), ('image', 'NN'), ('analogies', 'NNS'), ('using', 'VBG'), ('neural', 'JJ'), ('matching', 'NN'), ('and', 'CC'), ('blending', 'NN')]",['Generate']
107 | "Analyzing 1.8M tweets from Super Bowl 50 (Twython, Twitter API, AYLIEN)",4 points by mikewally 20 days ago  | discuss,20,"analyzing,1,8m,tweets,super,bowl,50,twython,twitter,api,aylien","Analyzing 1.8M tweets from Super Bowl 50 (Twython, Twitter API, AYLIEN)","Analyzing 1.8M tweets from Super Bowl 50 (Twython, Twitter API, AYLIEN)","[('Analyzing', 'VBG'), ('1', 'CD'), ('.', '.'), ('8M', 'CD'), ('tweets', 'NNS'), ('from', 'IN'), ('Super', 'NNP'), ('Bowl', 'NNP'), ('50', 'CD'), ('(', '('), ('Twython', 'NNP'), (',', ','), ('Twitter', 'NNP'), ('API', 'NNP'), (',', ','), ('AYLIEN', 'NNP'), (')', ')')]","['Super Bowl', 'Twython', 'Twitter API', 'AYLIEN']"
108 | Newly released sklearn compatible library of categorical encoders,7 points by wdm0006 25 days ago  | discuss,25,"newly,released,sklearn,compatible,library,categorical,encoders",Newly released sklearn compatible library of categorical encod,Newly released sklearn compatible library of categorical encoders,"[('Newly', 'RB'), ('released', 'VBN'), ('sklearn', 'NN'), ('compatible', 'JJ'), ('library', 'NN'), ('of', 'IN'), ('categorical', 'JJ'), ('encoders', 'NNS')]",[]
109 | Watch Tiny Neural Nets Learn,4 points by swanint 20 days ago  | discuss,20,"watch,tiny,neural,nets,learn",Watch Tiny Neural Nets Learn,Watch Tiny Neural Nets Learn,"[('Watch', 'NNP'), ('Tiny', 'NNP'), ('Neural', 'NNP'), ('Nets', 'NNP'), ('Learn', 'NNP')]",['Watch Tiny Neural Nets Learn']
110 | Four pitfalls of hill climbing: An animated look,5 points by csaid81 22 days ago  | discuss,22,"four,pitfalls,hill,climbing,animated,look",Four pitfalls of hill climbing: An animated look,Four pitfalls of hill climbing: An animated look,"[('Four', 'CD'), ('pitfalls', 'NNS'), ('of', 'IN'), ('hill', 'NN'), ('climbing', 'VBG'), (':', ':'), ('An', 'DT'), ('animated', 'JJ'), ('look', 'NN')]",[]
111 | "Decision Forests, Convolutional Networks and the Models in-Between",2 points by ebellm 15 days ago  | discuss,15,"decision,forests,convolutional,networks,models","Decision Forests, Convolutional Networks and the Models in-Between","Decision Forests, Convolutional Networks and the Models in-Between","[('Decision', 'NN'), ('Forests', 'NNS'), (',', ','), ('Convolutional', 'NNP'), ('Networks', 'NNP'), ('and', 'CC'), ('the', 'DT'), ('Models', 'NNP'), ('in', 'IN'), ('-', ':'), ('Between', 'NN')]","['Convolutional Networks', 'Models']"
112 | How a Math Genius Hacked OkCupid to Find True Love,15 points by roh_codeur 34 days ago  | discuss,34,"math,genius,hacked,okcupid,find,true,love",How a Math Genius Hacked OkCupid to Find True Lov,How a Math Genius Hacked OkCupid to Find True Love,"[('How', 'WRB'), ('a', 'DT'), ('Math', 'NNP'), ('Genius', 'NNP'), ('Hacked', 'NNP'), ('OkCupid', 'NNP'), ('to', 'TO'), ('Find', 'VB'), ('True', 'JJ'), ('Love', 'NNP')]",['True Love']
113 | No developers for PyLearn2,3 points by tfturing 18 days ago  | discuss,18,"developers,pylearn2",No developers for PyLearn2,No developers for PyLearn2,"[('No', 'DT'), ('developers', 'NNS'), ('for', 'IN'), ('PyLearn2', 'NN')]",['PyLearn2']
114 | Density Estimation with Dirichlet Process Mixtures using PyMC3,6 points by MidsizeBlowfish 25 days ago  | discuss,25,"density,estimation,dirichlet,process,mixtures,using,pymc3",Density Estimation with Dirichlet Process Mixtures using PyMC3,Density Estimation with Dirichlet Process Mixtures using PyMC3,"[('Density', 'NNP'), ('Estimation', 'NNP'), ('with', 'IN'), ('Dirichlet', 'NNP'), ('Process', 'NNP'), ('Mixtures', 'NNP'), ('using', 'VBG'), ('PyMC3', 'NNP')]","['Density Estimation', 'Dirichlet Process Mixtures', 'PyMC3']"
115 | Using survival analysis and git-pandas to estimate code quality,3 points by wdm0006 19 days ago  | discuss,19,"using,survival,analysis,git,pandas,estimate,code,quality",Using survival analysis and git-pandas to estimate code qu,Using survival analysis and git-pandas to estimate code quality,"[('Using', 'VBG'), ('survival', 'JJ'), ('analysis', 'NN'), ('and', 'CC'), ('git', 'JJ'), ('-', ':'), ('pandas', 'NN'), ('to', 'TO'), ('estimate', 'VB'), ('code', 'NN'), ('quality', 'NN')]",[]
116 | An Analysis of the Flint Michigan Water Crisis: Part 1 Initial Corrosivity,3 points by JHorn 19 days ago  | discuss,19,"analysis,flint,michigan,water,crisis,part,1,initial,corrosivity",An Analysis of the Flint Michigan Water Crisis: Part 1 Initial Corros,An Analysis of the Flint Michigan Water Crisis: Part 1 Initial Corrosivity,"[('An', 'DT'), ('Analysis', 'NN'), ('of', 'IN'), ('the', 'DT'), ('Flint', 'NNP'), ('Michigan', 'NNP'), ('Water', 'NNP'), ('Crisis', 'NNP'), (':', ':'), ('Part', 'NN'), ('1', 'CD'), ('Initial', 'NNP'), ('Corrosivity', 'NNP')]",['Flint Michigan Water']
117 | An Analysis of Republican Twitter Follower Interests,6 points by michelangelo 25 days ago  | discuss,25,"analysis,republican,twitter,follower,interests",An Analysis of Republican Twitter Follower Interest,An Analysis of Republican Twitter Follower Interests,"[('An', 'DT'), ('Analysis', 'NN'), ('of', 'IN'), ('Republican', 'JJ'), ('Twitter', 'NNP'), ('Follower', 'NNP'), ('Interests', 'NNS')]",['Republican Twitter Follower']
118 | Introduction to ML talk,8 points by cjbayesian 29 days ago  | discuss,29,"introduction,ml,talk",Introduction to ML talk,Introduction to ML talk,"[('Introduction', 'NN'), ('to', 'TO'), ('ML', 'NNP'), ('talk', 'NN')]",[]
119 | GloVe vs word2vec revisited,3 points by pmigdal 20 days ago  | discuss,20,"glove,vs,word2vec,revisited",GloVe vs word2vec revisit,GloVe vs word2vec revisited,"[('GloVe', 'NNP'), ('vs', 'NN'), ('word2vec', 'NN'), ('revisited', 'VBD')]",['GloVe']
120 | Undergrad Data Analysis/Science internships SF Bay?,3 points by tctctc 15 days ago  | 5 comments,15,"undergrad,data,analysis,science,internships,sf,bay",Undergrad Data Analysis/Science internships SF Bay?,Undergrad Data Analysis/Science internships SF Bay?,"[('Undergrad', 'NNP'), ('Data', 'NNP'), ('Analysis', 'NNP'), ('/', 'NNP'), ('Science', 'NNP'), ('internships', 'NNS'), ('SF', 'NNP'), ('Bay', 'NNP'), ('?', '.')]",['Undergrad Data Analysis']
121 | The Role of Statistical Significance in Growth Hacking,6 points by rawls234 26 days ago  | discuss,26,"role,statistical,significance,growth,hacking",The Role of Statistical Significance in Growth Hack,The Role of Statistical Significance in Growth Hacking,"[('The', 'DT'), ('Role', 'NNP'), ('of', 'IN'), ('Statistical', 'NNP'), ('Significance', 'NNP'), ('in', 'IN'), ('Growth', 'NNP'), ('Hacking', 'NNP')]","['Statistical Significance', 'Growth Hacking']"
122 | Data Science Course @ Harvard,7 points by rahmaniacc 28 days ago  | 2 comments,28,"data,science,course,@,harvard",Data Science Course @ Harvard,Data Science Course @ Harvard,"[('Data', 'NNP'), ('Science', 'NNP'), ('Course', 'NNP'), ('@', 'NNP'), ('Harvard', 'NNP')]",['Data Science Course']
123 | Principal Component Projection Without Principal Component Analysis,6 points by genofon 27 days ago  | discuss,27,"principal,component,projection,without,principal,component,analysis",Principal Component Projection Without Principal Component Analysi,Principal Component Projection Without Principal Component Analysis,"[('Principal', 'JJ'), ('Component', 'NNP'), ('Projection', 'NNP'), ('Without', 'IN'), ('Principal', 'NNP'), ('Component', 'NNP'), ('Analysis', 'NN')]",['Principal']
124 | "Machine Learning: An In-Depth, Non-Technical Guide - Part 3",7 points by innoarchitech 29 days ago  | discuss,29,"machine,learning,depth,non,technical,guide,part,3","Machine Learning: An In-Depth, Non-Technical Guide - Part 3","Machine Learning: An In-Depth, Non-Technical Guide - Part 3","[('Machine', 'NN'), ('Learning', 'NNP'), (':', ':'), ('An', 'DT'), ('In', 'IN'), ('-', ':'), ('Depth', 'NN'), (',', ','), ('Non', 'NNP'), ('-', ':'), ('Technical', 'NNP'), ('Guide', 'NNP'), ('-', ':'), ('Part', 'NN'), ('3', 'CD')]","['Machine Learning', 'Non', 'Technical Guide']"
125 | Stochastic Dummy Boosting,2 points by mikeskim 17 days ago  | discuss,17,"stochastic,dummy,boosting",Stochastic Dummy Boost,Stochastic Dummy Boosting,"[('Stochastic', 'JJ'), ('Dummy', 'NNP'), ('Boosting', 'NNP')]",['Stochastic Dummy']
126 | Interactive Map: Hong-Kong through The Lense of Instagram,2 points by BrianN 18 days ago  | discuss,18,"interactive,map,hong,kong,lense,instagram",Interactive Map: Hong-Kong through The Lense of Instagram,Interactive Map: Hong-Kong through The Lense of Instagram,"[('Interactive', 'JJ'), ('Map', 'NN'), (':', ':'), ('Hong', 'NNP'), ('-', ':'), ('Kong', 'NNP'), ('through', 'IN'), ('The', 'DT'), ('Lense', 'NNP'), ('of', 'IN'), ('Instagram', 'NNP')]","['Hong', 'Kong']"
127 | Data Science at Monsanto,3 points by doctorcroc 22 days ago  | discuss,22,"data,science,monsanto",Data Science at Monsanto,Data Science at Monsanto,"[('Data', 'NNP'), ('Science', 'NNP'), ('at', 'IN'), ('Monsanto', 'NNP')]","['Data Science', 'Monsanto']"
128 | Data Science at Instacart,11 points by jeremystan 34 days ago  | 3 comments,34,"data,science,instacart",Data Science at Instacart,Data Science at Instacart,"[('Data', 'NNP'), ('Science', 'NNP'), ('at', 'IN'), ('Instacart', 'NNP')]","['Data Science', 'Instacart']"
129 | Building a Streaming Search Platform,6 points by ddrum001 27 days ago  | discuss,27,"building,streaming,search,platform",Building a Streaming Search Platform,Building a Streaming Search Platform,"[('Building', 'VBG'), ('a', 'DT'), ('Streaming', 'NNP'), ('Search', 'NNP'), ('Platform', 'NNP')]",[]
130 | A Sneak Peak of the Cloud: the 2 Minute Intro for Beginners,2 points by andymaheshw 19 days ago  | discuss,19,"sneak,peak,cloud,2,minute,intro,beginners",A Sneak Peak of the Cloud: the 2 Minute Intro for Beginn,A Sneak Peak of the Cloud: the 2 Minute Intro for Beginners,"[('A', 'DT'), ('Sneak', 'NNP'), ('Peak', 'NNP'), ('of', 'IN'), ('the', 'DT'), ('Cloud', 'NNP'), (':', ':'), ('the', 'DT'), ('2', 'CD'), ('Minute', 'NNP'), ('Intro', 'NNP'), ('for', 'IN'), ('Beginners', 'NNP')]",['Sneak Peak']
131 | Win-Vector video courses: price/status changes,2 points by jmount 19 days ago  | discuss,19,"win,vector,video,courses,price,status,changes",Win-Vector video courses: price/status chang,Win-Vector video courses: price/status changes,"[('Win', 'NNP'), ('-', ':'), ('Vector', 'NNP'), ('video', 'NN'), ('courses', 'NNS'), (':', ':'), ('price', 'NN'), ('/', 'NN'), ('status', 'NN'), ('changes', 'NNS')]","['Win', 'Vector']"
132 | 50+ Data Science and Machine Learning Cheat Sheets,20 points by elyase 42 days ago  | 1 comment,42,"50,+,data,science,machine,learning,cheat,sheets",50+ Data Science and Machine Learning Cheat Sheet,50+ Data Science and Machine Learning Cheat Sheets,"[('50', 'CD'), ('+', 'JJ'), ('Data', 'NNP'), ('Science', 'NNP'), ('and', 'CC'), ('Machine', 'NNP'), ('Learning', 'NNP'), ('Cheat', 'NNP'), ('Sheets', 'NNS')]","['Data Science', 'Machine Learning Cheat']"
133 | One More Reason Not To Be Scared of Deep Learning,2 points by amplifier_khan 21 days ago  | discuss,21,"one,reason,scared,deep,learning",One More Reason Not To Be Scared of Deep Learn,One More Reason Not To Be Scared of Deep Learning,"[('One', 'CD'), ('More', 'JJR'), ('Reason', 'NNP'), ('Not', 'RB'), ('To', 'TO'), ('Be', 'VB'), ('Scared', 'NNP'), ('of', 'IN'), ('Deep', 'NNP'), ('Learning', 'NNP')]","['Reason', 'Deep Learning']"
134 | Visual Logic Authoring vs Code,2 points by AnnaOnTheWeb 21 days ago  | discuss,21,"visual,logic,authoring,vs,code",Visual Logic Authoring vs Cod,Visual Logic Authoring vs Code,"[('Visual', 'JJ'), ('Logic', 'NNP'), ('Authoring', 'NNP'), ('vs', 'NN'), ('Code', 'NNP')]",['Visual Logic']
135 | Data Science in Python online training with hands-on experience,2 points by Puneet 21 days ago  | discuss,21,"data,science,python,online,training,hands,experience",Data Science in Python online training with hands-on experi,Data Science in Python online training with hands-on experience,"[('Data', 'NNP'), ('Science', 'NNP'), ('in', 'IN'), ('Python', 'NNP'), ('online', 'JJ'), ('training', 'NN'), ('with', 'IN'), ('hands', 'NNS'), ('-', ':'), ('on', 'IN'), ('experience', 'NN')]","['Data Science', 'Python']"
136 | Viewing the US Presidential Primary Through the Lens of Twitter,8 points by michelangelo 32 days ago  | discuss,32,"viewing,us,presidential,primary,lens,twitter",Viewing the US Presidential Primary Through the Lens of Twitt,Viewing the US Presidential Primary Through the Lens of Twitter,"[('Viewing', 'VBG'), ('the', 'DT'), ('US', 'NNP'), ('Presidential', 'NNP'), ('Primary', 'NNP'), ('Through', 'IN'), ('the', 'DT'), ('Lens', 'NNP'), ('of', 'IN'), ('Twitter', 'NNP')]",['US']
137 | Caffe on Spark open sourced,4 points by rahmaniacc 26 days ago  | discuss,26,"caffe,spark,open,sourced",Caffe on Spark open sourc,Caffe on Spark open sourced,"[('Caffe', 'NNP'), ('on', 'IN'), ('Spark', 'NNP'), ('open', 'JJ'), ('sourced', 'VBD')]","['Caffe', 'Spark']"
138 | The Ethical Data Scientist,5 points by tfturing 28 days ago  | discuss,28,"ethical,data,scientist",The Ethical Data Scientist,The Ethical Data Scientist,"[('The', 'DT'), ('Ethical', 'NNP'), ('Data', 'NNP'), ('Scientist', 'NN')]",['Ethical Data']
139 | Answers to Frequently Asked Questions in Machine Learning,3 points by rasbt 21 days ago  | discuss,21,"answers,frequently,asked,questions,machine,learning",Answers to Frequently Asked Questions in Machine Learn,Answers to Frequently Asked Questions in Machine Learning,"[('Answers', 'NNS'), ('to', 'TO'), ('Frequently', 'NNP'), ('Asked', 'NNP'), ('Questions', 'NNS'), ('in', 'IN'), ('Machine', 'NNP'), ('Learning', 'NNP')]","['Frequently Asked', 'Machine Learning']"
140 | Intro to A/B Testing and P-Values,2 points by randyzwitch 22 days ago  | discuss,22,"intro,b,testing,p,values",Intro to A/B Testing and P-Valu,Intro to A/B Testing and P-Values,"[('Intro', 'NNP'), ('to', 'TO'), ('A', 'NNP'), ('/', 'NNP'), ('B', 'NNP'), ('Testing', 'NNP'), ('and', 'CC'), ('P', 'NNP'), ('-', ':'), ('Values', 'NNS')]",['Intro']
141 | Visualizing State Level Data With R and Statebins,2 points by usujason 22 days ago  | discuss,22,"visualizing,state,level,data,r,statebins",Visualizing State Level Data With R and Statebin,Visualizing State Level Data With R and Statebins,"[('Visualizing', 'VBG'), ('State', 'NNP'), ('Level', 'NNP'), ('Data', 'NNP'), ('With', 'IN'), ('R', 'NNP'), ('and', 'CC'), ('Statebins', 'NNP')]",[]
142 | "Probabilistic Graphical Models slides & video lectures (Eric Xing, CMU)",4 points by ororm 27 days ago  | discuss,27,"probabilistic,graphical,models,slides,&,video,lectures,eric,xing,cmu","Probabilistic Graphical Models slides & video lectures (Eric Xing, CMU)","Probabilistic Graphical Models slides & video lectures (Eric Xing, CMU)","[('Probabilistic', 'JJ'), ('Graphical', 'NNP'), ('Models', 'NNP'), ('slides', 'VBZ'), ('&', 'CC'), ('video', 'NN'), ('lectures', 'NNS'), ('(', '('), ('Eric', 'NNP'), ('Xing', 'NNP'), (',', ','), ('CMU', 'NNP'), (')', ')')]","['Eric Xing', 'CMU']"
143 | Sense2vec with spaCy and Gensim,9 points by elyase 35 days ago  | 2 comments,35,"sense2vec,spacy,gensim",Sense2vec with spaCy and Gensim,Sense2vec with spaCy and Gensim,"[('Sense2vec', 'NN'), ('with', 'IN'), ('spaCy', 'NN'), ('and', 'CC'), ('Gensim', 'NNP')]","['Sense2vec', 'spaCy', 'Gensim']"
144 | How to Code and Understand DeepMind's Neural Stack Machine (in Python),2 points by genofon 23 days ago  | discuss,23,"code,understand,deepmind,neural,stack,machine,python",How to Code and Understand DeepMind's Neural Stack Machine (in Python),How to Code and Understand DeepMind's Neural Stack Machine (in Python),"[('How', 'WRB'), ('to', 'TO'), ('Code', 'NNP'), ('and', 'CC'), ('Understand', 'NNP'), ('DeepMind', 'NNP'), (""'"", 'POS'), ('s', 'JJ'), ('Neural', 'NNP'), ('Stack', 'NNP'), ('Machine', 'NNP'), ('(', '('), ('in', 'IN'), ('Python', 'NNP'), (')', ')')]","['Code', 'Understand DeepMind', 'Python']"
145 | How to make polished Jupyter presentations with optional code visibility,9 points by csaid81 36 days ago  | discuss,36,"make,polished,jupyter,presentations,optional,code,visibility",How to make polished Jupyter presentations with optional code vis,How to make polished Jupyter presentations with optional code visibility,"[('How', 'WRB'), ('to', 'TO'), ('make', 'VB'), ('polished', 'JJ'), ('Jupyter', 'NNP'), ('presentations', 'NNS'), ('with', 'IN'), ('optional', 'JJ'), ('code', 'NN'), ('visibility', 'NN')]",[]
146 | How to become a Bayesian in eight easy steps,17 points by EtzA 43 days ago  | 1 comment,43,"become,bayesian,eight,easy,steps",How to become a Bayesian in eight easy step,How to become a Bayesian in eight easy steps,"[('How', 'WRB'), ('to', 'TO'), ('become', 'VB'), ('a', 'DT'), ('Bayesian', 'JJ'), ('in', 'IN'), ('eight', 'CD'), ('easy', 'JJ'), ('steps', 'NNS')]",['Bayesian']
147 | Optimizing .*:  Details of Vectorization and Metaprogramming in Julia,4 points by randyzwitch 29 days ago  | discuss,29,"optimizing,.*:,details,vectorization,metaprogramming,julia",Optimizing .*:  Details of Vectorization and Metaprogramming in Julia,Optimizing .*:  Details of Vectorization and Metaprogramming in Julia,"[('Optimizing', 'VBG'), ('.*:', 'NNP'), ('Details', 'NNP'), ('of', 'IN'), ('Vectorization', 'NNP'), ('and', 'CC'), ('Metaprogramming', 'NNP'), ('in', 'IN'), ('Julia', 'NNP')]",['Julia']
148 | IBM certified Apache Spark Online Training,8 points by divya_jain 35 days ago  | discuss,35,"ibm,certified,apache,spark,online,training",IBM certified Apache Spark Online Train,IBM certified Apache Spark Online Training,"[('IBM', 'NNP'), ('certified', 'VBD'), ('Apache', 'NNP'), ('Spark', 'NNP'), ('Online', 'NNP'), ('Training', 'NN')]","['IBM', 'Apache Spark Online']"
149 | Geographic Data Science course,2 points by rk 24 days ago  | discuss,24,"geographic,data,science,course",Geographic Data Science cours,Geographic Data Science course,"[('Geographic', 'NNP'), ('Data', 'NNP'), ('Science', 'NNP'), ('course', 'NN')]",['Geographic Data Science']
150 | "The Daily Mail Stole My Visualization, Twice",5 points by thehoff 32 days ago  | 1 comment,32,"daily,mail,stole,visualization,twice","The Daily Mail Stole My Visualization, Twic","The Daily Mail Stole My Visualization, Twice","[('The', 'DT'), ('Daily', 'NNP'), ('Mail', 'NNP'), ('Stole', 'NNP'), ('My', 'NNP'), ('Visualization', 'NNP'), (',', ','), ('Twice', 'NNP')]",['Daily Mail Stole My Visualization']
151 | Ensemble Methods: Improved Machine Learning Results,9 points by PyBloggers 37 days ago  | discuss,37,"ensemble,methods,improved,machine,learning,results",Ensemble Methods: Improved Machine Learning Result,Ensemble Methods: Improved Machine Learning Results,"[('Ensemble', 'JJ'), ('Methods', 'NNS'), (':', ':'), ('Improved', 'VBN'), ('Machine', 'NNP'), ('Learning', 'NNP'), ('Results', 'NNP')]",['Machine Learning Results']
152 | Apache Spark and unsupervised learning in security,2 points by gradientflow 26 days ago  | discuss,26,"apache,spark,unsupervised,learning,security",Apache Spark and unsupervised learning in secur,Apache Spark and unsupervised learning in security,"[('Apache', 'NNP'), ('Spark', 'NNP'), ('and', 'CC'), ('unsupervised', 'JJ'), ('learning', 'NN'), ('in', 'IN'), ('security', 'NN')]",['Apache Spark']
153 | MachineJS: Automated machine learning- just give it a data file!,2 points by dsernst 26 days ago  | discuss,26,"machinejs,automated,machine,learning,give,data,file",MachineJS: Automated machine learning- just give it a data file!,MachineJS: Automated machine learning- just give it a data file!,"[('MachineJS', 'NN'), (':', ':'), ('Automated', 'VBN'), ('machine', 'NN'), ('learning', 'VBG'), ('-', ':'), ('just', 'RB'), ('give', 'VB'), ('it', 'PRP'), ('a', 'DT'), ('data', 'NN'), ('file', 'NN'), ('!', '.')]",['MachineJS']
154 | Kafka Producer Latency with Large Topic Counts,2 points by marklit 26 days ago  | discuss,26,"kafka,producer,latency,large,topic,counts",Kafka Producer Latency with Large Topic Count,Kafka Producer Latency with Large Topic Counts,"[('Kafka', 'NNP'), ('Producer', 'NNP'), ('Latency', 'NNP'), ('with', 'IN'), ('Large', 'NNP'), ('Topic', 'NNP'), ('Counts', 'NNP')]","['Kafka Producer Latency', 'Large Topic Counts']"
155 | The NSA?s SKYNET program may be killing thousands of innocent people,6 points by zlipp 35 days ago  | discuss,35,"nsa,skynet,program,may,killing,thousands,innocent,people",The NSA?s SKYNET program may be killing thousands of innocent peopl,The NSA?s SKYNET program may be killing thousands of innocent people,"[('The', 'DT'), ('NSA', 'NNP'), ('?', '.'), ('s', 'JJ'), ('SKYNET', 'NNP'), ('program', 'NN'), ('may', 'MD'), ('be', 'VB'), ('killing', 'VBG'), ('thousands', 'NNS'), ('of', 'IN'), ('innocent', 'JJ'), ('people', 'NNS')]",['NSA']
156 | Overoptimizing: a story about kaggle,3 points by wdm0006 29 days ago  | discuss,29,"overoptimizing,story,kaggle",Overoptimizing: a story about kaggl,Overoptimizing: a story about kaggle,"[('Overoptimizing', 'NN'), (':', ':'), ('a', 'DT'), ('story', 'NN'), ('about', 'IN'), ('kaggle', 'NN')]",[]
157 | "Big Dimensions, and What You Can Do About It",2 points by ramsey 26 days ago  | discuss,26,"big,dimensions","Big Dimensions, and What You Can Do About It","Big Dimensions, and What You Can Do About It","[('Big', 'JJ'), ('Dimensions', 'NNS'), (',', ','), ('and', 'CC'), ('What', 'WP'), ('You', 'PRP'), ('Can', 'MD'), ('Do', 'VB'), ('About', 'IN'), ('It', 'PRP')]",[]
158 | Automate Your Oscars Pool with R,2 points by jamesdreiss 27 days ago  | discuss,27,"automate,oscars,pool,r",Automate Your Oscars Pool with R,Automate Your Oscars Pool with R,"[('Automate', 'VB'), ('Your', 'PRP$'), ('Oscars', 'NNP'), ('Pool', 'NNP'), ('with', 'IN'), ('R', 'NNP')]",['Oscars Pool']
159 | Signal Processing with LIGO GW150914 data,9 points by tfturing 39 days ago  | discuss,39,"signal,processing,ligo,gw150914,data",Signal Processing with LIGO GW150914 data,Signal Processing with LIGO GW150914 data,"[('Signal', 'JJ'), ('Processing', 'VBG'), ('with', 'IN'), ('LIGO', 'NNP'), ('GW150914', 'NNP'), ('data', 'NNS')]",['LIGO']
160 | Overview of DeZyre and Coursera Data Science Course,5 points by ann928 34 days ago  | discuss,34,"overview,dezyre,coursera,data,science,course",Overview of DeZyre and Coursera Data Science Cours,Overview of DeZyre and Coursera Data Science Course,"[('Overview', 'NN'), ('of', 'IN'), ('DeZyre', 'NNP'), ('and', 'CC'), ('Coursera', 'NNP'), ('Data', 'NNP'), ('Science', 'NNP'), ('Course', 'NNP')]","['Overview', 'DeZyre', 'Coursera Data Science Course']"
161 | Upcoming Datathon in NYC,2 points by VicTrey 27 days ago  | discuss,27,"upcoming,datathon,nyc",Upcoming Datathon in NYC,Upcoming Datathon in NYC,"[('Upcoming', 'VBG'), ('Datathon', 'NNP'), ('in', 'IN'), ('NYC', 'NNP')]","['Datathon', 'NYC']"
162 | Summarizing Data in SQL,15 points by elisebreda 46 days ago  | discuss,46,"summarizing,data,sql",Summarizing Data in SQL,Summarizing Data in SQL,"[('Summarizing', 'VBG'), ('Data', 'NNP'), ('in', 'IN'), ('SQL', 'NNP')]",['SQL']
163 | A/B Testing for Scammers,2 points by sameermanek 28 days ago  | discuss,28,"b,testing,scammers",A/B Testing for Scamm,A/B Testing for Scammers,"[('A', 'DT'), ('/', 'NN'), ('B', 'NNP'), ('Testing', 'NNP'), ('for', 'IN'), ('Scammers', 'NNP')]",[]
164 | Highly interpretable classifiers for scikit learn using Bayesian decision rules,2 points by mcnulty 28 days ago  | discuss,28,"highly,interpretable,classifiers,scikit,learn,using,bayesian,decision,rules",Highly interpretable classifiers for scikit learn using Bayesian decision rul,Highly interpretable classifiers for scikit learn using Bayesian decision rules,"[('Highly', 'NNP'), ('interpretable', 'JJ'), ('classifiers', 'NNS'), ('for', 'IN'), ('scikit', 'NN'), ('learn', 'NN'), ('using', 'VBG'), ('Bayesian', 'JJ'), ('decision', 'NN'), ('rules', 'NNS')]","['Highly', 'Bayesian']"
165 | Auto-scaling scikit-learn with Spark,11 points by falaki 42 days ago  | discuss,42,"auto,scaling,scikit,learn,spark",Auto-scaling scikit-learn with Spark,Auto-scaling scikit-learn with Spark,"[('Auto', 'NNP'), ('-', ':'), ('scaling', 'VBG'), ('scikit', 'JJ'), ('-', ':'), ('learn', 'NN'), ('with', 'IN'), ('Spark', 'NNP')]","['Auto', 'Spark']"
166 | Where the f*** can I park?,2 points by manugarri 29 days ago  | discuss,29,"f,***,park",Where the f*** can I park?,Where the f*** can I park?,"[('Where', 'WRB'), ('the', 'DT'), ('f', 'NN'), ('***', 'NN'), ('can', 'MD'), ('I', 'PRP'), ('park', 'VB'), ('?', '.')]",[]
167 | "Machine Learning: An In-Depth, Non-Technical Guide - Part 2",5 points by innoarchitech 36 days ago  | discuss,36,"machine,learning,depth,non,technical,guide,part,2","Machine Learning: An In-Depth, Non-Technical Guide - Part 2","Machine Learning: An In-Depth, Non-Technical Guide - Part 2","[('Machine', 'NN'), ('Learning', 'NNP'), (':', ':'), ('An', 'DT'), ('In', 'IN'), ('-', ':'), ('Depth', 'NN'), (',', ','), ('Non', 'NNP'), ('-', ':'), ('Technical', 'NNP'), ('Guide', 'NNP'), ('-', ':'), ('Part', 'NN'), ('2', 'CD')]","['Machine Learning', 'Non', 'Technical Guide']"
168 | Webhose.io now offers a historical data archive,7 points by databuffer 40 days ago  | discuss,40,"webhose,io,offers,historical,data,archive",Webhose.io now offers a historical data arch,Webhose.io now offers a historical data archive,"[('Webhose', 'NNP'), ('.', '.'), ('io', 'NN'), ('now', 'RB'), ('offers', 'VBZ'), ('a', 'DT'), ('historical', 'JJ'), ('data', 'NN'), ('archive', 'NN')]",['Webhose']
169 | Meetup: Introduction to Machine Learning Algorithms for Data Science.,4 points by ann928 35 days ago  | discuss,35,"meetup,introduction,machine,learning,algorithms,data,science",Meetup: Introduction to Machine Learning Algorithms for Data Science.,Meetup: Introduction to Machine Learning Algorithms for Data Science.,"[('Meetup', 'NN'), (':', ':'), ('Introduction', 'NN'), ('to', 'TO'), ('Machine', 'NNP'), ('Learning', 'NNP'), ('Algorithms', 'NNP'), ('for', 'IN'), ('Data', 'NNP'), ('Science', 'NNP'), ('.', '.')]","['Meetup', 'Machine Learning Algorithms', 'Data Science']"
170 | Exploring the Limits of Language Modeling,8 points by soates 42 days ago  | discuss,42,"exploring,limits,language,modeling",Exploring the Limits of Language Model,Exploring the Limits of Language Modeling,"[('Exploring', 'VBG'), ('the', 'DT'), ('Limits', 'NNS'), ('of', 'IN'), ('Language', 'NNP'), ('Modeling', 'NNP')]",['Language Modeling']
171 | Text Mining South Park,7 points by pmigdal 41 days ago  | discuss,41,"text,mining,south,park",Text Mining South Park,Text Mining South Park,"[('Text', 'NNP'), ('Mining', 'NNP'), ('South', 'NNP'), ('Park', 'NNP')]",['Text Mining South Park']
172 | Finding the K in K-means by Parametric Bootstrap,7 points by jmount 41 days ago  | 1 comment,41,"finding,k,k,means,parametric,bootstrap",Finding the K in K-means by Parametric Bootstrap,Finding the K in K-means by Parametric Bootstrap,"[('Finding', 'VBG'), ('the', 'DT'), ('K', 'NNP'), ('in', 'IN'), ('K', 'NNP'), ('-', ':'), ('means', 'NNS'), ('by', 'IN'), ('Parametric', 'NNP'), ('Bootstrap', 'NNP')]",['Parametric Bootstrap']
173 | A Billion NYC Taxi and Uber Rides in AWS Redshift,2 points by marklit 31 days ago  | discuss,31,"billion,nyc,taxi,uber,rides,aws,redshift",A Billion NYC Taxi and Uber Rides in AWS Redshift,A Billion NYC Taxi and Uber Rides in AWS Redshift,"[('A', 'DT'), ('Billion', 'NNP'), ('NYC', 'NNP'), ('Taxi', 'NNP'), ('and', 'CC'), ('Uber', 'NNP'), ('Rides', 'NNP'), ('in', 'IN'), ('AWS', 'NNP'), ('Redshift', 'NNP')]",['AWS Redshift']
174 | Getting Started with Statistics for Data Science,3 points by nickhould 34 days ago  | discuss,34,"getting,started,statistics,data,science",Getting Started with Statistics for Data Sci,Getting Started with Statistics for Data Science,"[('Getting', 'VBG'), ('Started', 'VBN'), ('with', 'IN'), ('Statistics', 'NNS'), ('for', 'IN'), ('Data', 'NNP'), ('Science', 'NNP')]",['Data Science']
175 | Rodeo 1.3 - Tab-completion for docstrings,3 points by glamp 35 days ago  | discuss,35,"rodeo,1,3,tab,completion,docstrings",Rodeo 1.3 - Tab-completion for docstr,Rodeo 1.3 - Tab-completion for docstrings,"[('Rodeo', 'NN'), ('1', 'CD'), ('.', '.'), ('3', 'CD'), ('-', ':'), ('Tab', 'NNP'), ('-', ':'), ('completion', 'NN'), ('for', 'IN'), ('docstrings', 'NNS')]",['Tab']
176 | Teaching D3.js - links,3 points by pmigdal 35 days ago  | discuss,35,"teaching,d3,js,links",Teaching D3.js - link,Teaching D3.js - links,"[('Teaching', 'VBG'), ('D3', 'NNP'), ('.', '.'), ('js', 'NN'), ('-', ':'), ('links', 'NNS')]",[]
177 | Parallel scikit-learn on YARN,5 points by stijntonk 39 days ago  | discuss,39,"parallel,scikit,learn,yarn",Parallel scikit-learn on YARN,Parallel scikit-learn on YARN,"[('Parallel', 'NNP'), ('scikit', 'SYM'), ('-', ':'), ('learn', 'NN'), ('on', 'IN'), ('YARN', 'NN')]","['Parallel', 'YARN']"
178 | Meetup: Free Live Webinar on Prescriptive Analytics for Fun and Profit,2 points by ann928 32 days ago  | discuss,32,"meetup,free,live,webinar,prescriptive,analytics,fun,profit",Meetup: Free Live Webinar on Prescriptive Analytics for Fun and Profit,Meetup: Free Live Webinar on Prescriptive Analytics for Fun and Profit,"[('Meetup', 'NN'), (':', ':'), ('Free', 'JJ'), ('Live', 'NNP'), ('Webinar', 'NNP'), ('on', 'IN'), ('Prescriptive', 'NNP'), ('Analytics', 'NNP'), ('for', 'IN'), ('Fun', 'NNP'), ('and', 'CC'), ('Profit', 'NN')]","['Meetup', 'Fun']"
179 | Access to VK.com (Vkontakte) API via R,2 points by dementiy 32 days ago  | discuss,32,"access,vk,com,vkontakte,api,via,r",Access to VK.com (Vkontakte) API via R,Access to VK.com (Vkontakte) API via R,"[('Access', 'NN'), ('to', 'TO'), ('VK', 'NNP'), ('.', '.'), ('com', 'NN'), ('(', '('), ('Vkontakte', 'NNP'), (')', ')'), ('API', 'NNP'), ('via', 'IN'), ('R', 'NNP')]",['Access']
180 |  	Deep Learning Tutorial by Y. LeCun and Y. Bengio,15 points by Anon84 50 days ago  | 1 comment,50,"deep,learning,tutorial,lecun,bengio", 	Deep Learning Tutorial by Y. LeCun and Y. Bengio, 	Deep Learning Tutorial by Y. LeCun and Y. Bengio,"[('Deep', 'NNP'), ('Learning', 'NNP'), ('Tutorial', 'NNP'), ('by', 'IN'), ('Y', 'NNP'), ('.', '.'), ('LeCun', 'NNP'), ('and', 'CC'), ('Y', 'NNP'), ('.', '.'), ('Bengio', 'NNP')]","['Deep Learning Tutorial', 'LeCun']"
181 | Machine Learning Meets Economics,20 points by nicolaskruchten 55 days ago  | discuss,55,"machine,learning,meets,economics",Machine Learning Meets Econom,Machine Learning Meets Economics,"[('Machine', 'NN'), ('Learning', 'NNP'), ('Meets', 'NNP'), ('Economics', 'NNP')]",['Machine Learning Meets Economics']
182 | 


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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Refine the Data"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 32,
 13 |    "metadata": {
 14 |     "collapsed": true
 15 |    },
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import pandas as pd"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 33,
 24 |    "metadata": {
 25 |     "collapsed": true
 26 |    },
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "df = pd.read_csv('data_tau.csv')"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 34,
 35 |    "metadata": {
 36 |     "collapsed": false
 37 |    },
 38 |    "outputs": [
 39 |     {
 40 |      "data": {
 41 |       "text/html": [
 42 |        "<div>\n",
 43 |        "<table border=\"1\" class=\"dataframe\">\n",
 44 |        "  <thead>\n",
 45 |        "    <tr style=\"text-align: right;\">\n",
 46 |        "      <th></th>\n",
 47 |        "      <th>title</th>\n",
 48 |        "      <th>date</th>\n",
 49 |        "    </tr>\n",
 50 |        "  </thead>\n",
 51 |        "  <tbody>\n",
 52 |        "    <tr>\n",
 53 |        "      <th>0</th>\n",
 54 |        "      <td>An Exploration of R, Yelp, and the Search for ...</td>\n",
 55 |        "      <td>5 points by Rogerh91 6 hours ago  | discuss</td>\n",
 56 |        "    </tr>\n",
 57 |        "    <tr>\n",
 58 |        "      <th>1</th>\n",
 59 |        "      <td>Deep Advances in Generative Modeling</td>\n",
 60 |        "      <td>7 points by gwulfs 15 hours ago  | 1 comment</td>\n",
 61 |        "    </tr>\n",
 62 |        "    <tr>\n",
 63 |        "      <th>2</th>\n",
 64 |        "      <td>Spark Pipelines: Elegant Yet Powerful</td>\n",
 65 |        "      <td>3 points by aouyang1 9 hours ago  | discuss</td>\n",
 66 |        "    </tr>\n",
 67 |        "    <tr>\n",
 68 |        "      <th>3</th>\n",
 69 |        "      <td>Shit VCs Say</td>\n",
 70 |        "      <td>3 points by Argentum01 10 hours ago  | discuss</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>4</th>\n",
 74 |        "      <td>Python, Machine Learning, and Language Wars</td>\n",
 75 |        "      <td>4 points by pmigdal 17 hours ago  | discuss</td>\n",
 76 |        "    </tr>\n",
 77 |        "  </tbody>\n",
 78 |        "</table>\n",
 79 |        "</div>"
 80 |       ],
 81 |       "text/plain": [
 82 |        "                                               title  \\\n",
 83 |        "0  An Exploration of R, Yelp, and the Search for ...   \n",
 84 |        "1               Deep Advances in Generative Modeling   \n",
 85 |        "2              Spark Pipelines: Elegant Yet Powerful   \n",
 86 |        "3                                       Shit VCs Say   \n",
 87 |        "4        Python, Machine Learning, and Language Wars   \n",
 88 |        "\n",
 89 |        "                                             date  \n",
 90 |        "0     5 points by Rogerh91 6 hours ago  | discuss  \n",
 91 |        "1    7 points by gwulfs 15 hours ago  | 1 comment  \n",
 92 |        "2     3 points by aouyang1 9 hours ago  | discuss  \n",
 93 |        "3  3 points by Argentum01 10 hours ago  | discuss  \n",
 94 |        "4     4 points by pmigdal 17 hours ago  | discuss  "
 95 |       ]
 96 |      },
 97 |      "execution_count": 34,
 98 |      "metadata": {},
 99 |      "output_type": "execute_result"
100 |     }
101 |    ],
102 |    "source": [
103 |     "df.head()"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "markdown",
108 |    "metadata": {},
109 |    "source": [
110 |     "To get the date of the title - we will need the following algorithm\n",
111 |     "- If the string contains **hours** we can consider it **1 day**\n",
112 |     "- And if the string has **day**, we pick the number preceding the **day**\n",
113 |     "\n",
114 |     "To apply this algorithm, we need to be able to pick these words and digits from a string. For that we will use Regular Expression."
115 |    ]
116 |   },
117 |   {
118 |    "cell_type": "markdown",
119 |    "metadata": {},
120 |    "source": [
121 |     "## Introduction to Regular Expression (Regex)\n",
122 |     "\n",
123 |     "Regular expression is a way of selecting text using symbols in a string.\n",
124 |     "\n",
125 |     "Refer to the following links for an interactive playground\n",
126 |     "- [http://regexr.com](http://regexr.com/)\n",
127 |     "- [http://regex101.com/](http://regex101.com/)"
128 |    ]
129 |   },
130 |   {
131 |    "cell_type": "code",
132 |    "execution_count": 35,
133 |    "metadata": {
134 |     "collapsed": true
135 |    },
136 |    "outputs": [],
137 |    "source": [
138 |     "import re"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 36,
144 |    "metadata": {
145 |     "collapsed": true
146 |    },
147 |    "outputs": [],
148 |    "source": [
149 |     "test_string = \"Hello world, welcome to 2016.\""
150 |    ]
151 |   },
152 |   {
153 |    "cell_type": "code",
154 |    "execution_count": 37,
155 |    "metadata": {
156 |     "collapsed": false
157 |    },
158 |    "outputs": [],
159 |    "source": [
160 |     "# We can pass the whole string and re.search will give the first occurence of the value\n",
161 |     "# re.search - This function searches for first occurrence of RE pattern within string.\n",
162 |     "a = re.search('Hello world, welcome to 2016',test_string)"
163 |    ]
164 |   },
165 |   {
166 |    "cell_type": "code",
167 |    "execution_count": 38,
168 |    "metadata": {
169 |     "collapsed": false
170 |    },
171 |    "outputs": [
172 |     {
173 |      "data": {
174 |       "text/plain": [
175 |        "<_sre.SRE_Match object; span=(0, 28), match='Hello world, welcome to 2016'>"
176 |       ]
177 |      },
178 |      "execution_count": 38,
179 |      "metadata": {},
180 |      "output_type": "execute_result"
181 |     }
182 |    ],
183 |    "source": [
184 |     "a"
185 |    ]
186 |   },
187 |   {
188 |    "cell_type": "code",
189 |    "execution_count": 39,
190 |    "metadata": {
191 |     "collapsed": false
192 |    },
193 |    "outputs": [
194 |     {
195 |      "data": {
196 |       "text/plain": [
197 |        "'Hello world, welcome to 2016'"
198 |       ]
199 |      },
200 |      "execution_count": 39,
201 |      "metadata": {},
202 |      "output_type": "execute_result"
203 |     }
204 |    ],
205 |    "source": [
206 |     "a.group()"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": 40,
212 |    "metadata": {
213 |     "collapsed": false
214 |    },
215 |    "outputs": [
216 |     {
217 |      "data": {
218 |       "text/plain": [
219 |        "'H'"
220 |       ]
221 |      },
222 |      "execution_count": 40,
223 |      "metadata": {},
224 |      "output_type": "execute_result"
225 |     }
226 |    ],
227 |    "source": [
228 |     "# Match the first letters in the string\n",
229 |     "a = re.search('.',test_string)\n",
230 |     "a.group()"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": 41,
236 |    "metadata": {
237 |     "collapsed": false
238 |    },
239 |    "outputs": [
240 |     {
241 |      "data": {
242 |       "text/plain": [
243 |        "'Hello world, welcome to 2016.'"
244 |       ]
245 |      },
246 |      "execution_count": 41,
247 |      "metadata": {},
248 |      "output_type": "execute_result"
249 |     }
250 |    ],
251 |    "source": [
252 |     "# Match all the letters in the string\n",
253 |     "a = re.search('.*',test_string)\n",
254 |     "a.group()"
255 |    ]
256 |   },
257 |   {
258 |    "cell_type": "code",
259 |    "execution_count": 42,
260 |    "metadata": {
261 |     "collapsed": false
262 |    },
263 |    "outputs": [
264 |     {
265 |      "name": "stdout",
266 |      "output_type": "stream",
267 |      "text": [
268 |       "<_sre.SRE_Match object; span=(0, 5), match='Hello'>\n"
269 |      ]
270 |     }
271 |    ],
272 |    "source": [
273 |     "a = re.search('Hello',test_string)\n",
274 |     "print(a)"
275 |    ]
276 |   },
277 |   {
278 |    "cell_type": "markdown",
279 |    "metadata": {},
280 |    "source": [
281 |     "** Some basic symbols**\n",
282 |     "\n",
283 |     "**`?`**   \n",
284 |     "\n",
285 |     "The question mark indicates zero or one occurrences of the preceding element. For example, colou?r matches both \"color\" and \"colour\".\n",
286 |     "\n",
287 |     "**`\\*`**\n",
288 |     "\n",
289 |     "The asterisk indicates zero or more occurrences of the preceding element. For example, ab*c matches \"ac\", \"abc\", \"abbc\", \"abbbc\", and so on.\n",
290 |     "\n",
291 |     "**`\\+`**\t\n",
292 |     "The plus sign indicates one or more occurrences of the preceding element. For example, ab+c matches \"abc\", \"abbc\", \"abbbc\", and so on, but not \"ac\".\n"
293 |    ]
294 |   },
295 |   {
296 |    "cell_type": "code",
297 |    "execution_count": 43,
298 |    "metadata": {
299 |     "collapsed": false
300 |    },
301 |    "outputs": [
302 |     {
303 |      "name": "stdout",
304 |      "output_type": "stream",
305 |      "text": [
306 |       "<_sre.SRE_Match object; span=(0, 2), match='He'>\n"
307 |      ]
308 |     }
309 |    ],
310 |    "source": [
311 |     "a = re.search('\\w.',test_string)\n",
312 |     "print(a)"
313 |    ]
314 |   },
315 |   {
316 |    "cell_type": "code",
317 |    "execution_count": 44,
318 |    "metadata": {
319 |     "collapsed": false
320 |    },
321 |    "outputs": [
322 |     {
323 |      "name": "stdout",
324 |      "output_type": "stream",
325 |      "text": [
326 |       "<_sre.SRE_Match object; span=(0, 5), match='Hello'>\n"
327 |      ]
328 |     }
329 |    ],
330 |    "source": [
331 |     "a = re.search('\\w*',test_string)\n",
332 |     "print(a)"
333 |    ]
334 |   },
335 |   {
336 |    "cell_type": "markdown",
337 |    "metadata": {},
338 |    "source": [
339 |     "### Exercises"
340 |    ]
341 |   },
342 |   {
343 |    "cell_type": "code",
344 |    "execution_count": 45,
345 |    "metadata": {
346 |     "collapsed": true
347 |    },
348 |    "outputs": [],
349 |    "source": [
350 |     "string = '''In 2016, we are learning Text Analytics in Data Science 101\n",
351 |     "            by scraping http://datatau.com'''"
352 |    ]
353 |   },
354 |   {
355 |    "cell_type": "code",
356 |    "execution_count": 46,
357 |    "metadata": {
358 |     "collapsed": false
359 |    },
360 |    "outputs": [],
361 |    "source": [
362 |     "string = \"In 2016, we are learning Text Analytics in Data Science 101 by scraping http://datatau.com\""
363 |    ]
364 |   },
365 |   {
366 |    "cell_type": "markdown",
367 |    "metadata": {},
368 |    "source": [
369 |     "Write a regex to pick the numbers 2016 from string above."
370 |    ]
371 |   },
372 |   {
373 |    "cell_type": "code",
374 |    "execution_count": null,
375 |    "metadata": {
376 |     "collapsed": true
377 |    },
378 |    "outputs": [],
379 |    "source": []
380 |   },
381 |   {
382 |    "cell_type": "markdown",
383 |    "metadata": {},
384 |    "source": [
385 |     "Write a regex to pick the url link (http://xyz.com) from the string above "
386 |    ]
387 |   },
388 |   {
389 |    "cell_type": "code",
390 |    "execution_count": null,
391 |    "metadata": {
392 |     "collapsed": true
393 |    },
394 |    "outputs": [],
395 |    "source": []
396 |   },
397 |   {
398 |    "cell_type": "markdown",
399 |    "metadata": {},
400 |    "source": [
401 |     "## Lets get the date from our string"
402 |    ]
403 |   },
404 |   {
405 |    "cell_type": "code",
406 |    "execution_count": 47,
407 |    "metadata": {
408 |     "collapsed": false
409 |    },
410 |    "outputs": [
411 |     {
412 |      "data": {
413 |       "text/html": [
414 |        "<div>\n",
415 |        "<table border=\"1\" class=\"dataframe\">\n",
416 |        "  <thead>\n",
417 |        "    <tr style=\"text-align: right;\">\n",
418 |        "      <th></th>\n",
419 |        "      <th>title</th>\n",
420 |        "      <th>date</th>\n",
421 |        "    </tr>\n",
422 |        "  </thead>\n",
423 |        "  <tbody>\n",
424 |        "    <tr>\n",
425 |        "      <th>0</th>\n",
426 |        "      <td>An Exploration of R, Yelp, and the Search for ...</td>\n",
427 |        "      <td>5 points by Rogerh91 6 hours ago  | discuss</td>\n",
428 |        "    </tr>\n",
429 |        "    <tr>\n",
430 |        "      <th>1</th>\n",
431 |        "      <td>Deep Advances in Generative Modeling</td>\n",
432 |        "      <td>7 points by gwulfs 15 hours ago  | 1 comment</td>\n",
433 |        "    </tr>\n",
434 |        "    <tr>\n",
435 |        "      <th>2</th>\n",
436 |        "      <td>Spark Pipelines: Elegant Yet Powerful</td>\n",
437 |        "      <td>3 points by aouyang1 9 hours ago  | discuss</td>\n",
438 |        "    </tr>\n",
439 |        "    <tr>\n",
440 |        "      <th>3</th>\n",
441 |        "      <td>Shit VCs Say</td>\n",
442 |        "      <td>3 points by Argentum01 10 hours ago  | discuss</td>\n",
443 |        "    </tr>\n",
444 |        "    <tr>\n",
445 |        "      <th>4</th>\n",
446 |        "      <td>Python, Machine Learning, and Language Wars</td>\n",
447 |        "      <td>4 points by pmigdal 17 hours ago  | discuss</td>\n",
448 |        "    </tr>\n",
449 |        "  </tbody>\n",
450 |        "</table>\n",
451 |        "</div>"
452 |       ],
453 |       "text/plain": [
454 |        "                                               title  \\\n",
455 |        "0  An Exploration of R, Yelp, and the Search for ...   \n",
456 |        "1               Deep Advances in Generative Modeling   \n",
457 |        "2              Spark Pipelines: Elegant Yet Powerful   \n",
458 |        "3                                       Shit VCs Say   \n",
459 |        "4        Python, Machine Learning, and Language Wars   \n",
460 |        "\n",
461 |        "                                             date  \n",
462 |        "0     5 points by Rogerh91 6 hours ago  | discuss  \n",
463 |        "1    7 points by gwulfs 15 hours ago  | 1 comment  \n",
464 |        "2     3 points by aouyang1 9 hours ago  | discuss  \n",
465 |        "3  3 points by Argentum01 10 hours ago  | discuss  \n",
466 |        "4     4 points by pmigdal 17 hours ago  | discuss  "
467 |       ]
468 |      },
469 |      "execution_count": 47,
470 |      "metadata": {},
471 |      "output_type": "execute_result"
472 |     }
473 |    ],
474 |    "source": [
475 |     "df.head()"
476 |    ]
477 |   },
478 |   {
479 |    "cell_type": "code",
480 |    "execution_count": 48,
481 |    "metadata": {
482 |     "collapsed": false
483 |    },
484 |    "outputs": [
485 |     {
486 |      "data": {
487 |       "text/html": [
488 |        "<div>\n",
489 |        "<table border=\"1\" class=\"dataframe\">\n",
490 |        "  <thead>\n",
491 |        "    <tr style=\"text-align: right;\">\n",
492 |        "      <th></th>\n",
493 |        "      <th>title</th>\n",
494 |        "      <th>date</th>\n",
495 |        "    </tr>\n",
496 |        "  </thead>\n",
497 |        "  <tbody>\n",
498 |        "    <tr>\n",
499 |        "      <th>175</th>\n",
500 |        "      <td>Getting Started with Statistics for Data Science</td>\n",
501 |        "      <td>3 points by nickhould 35 days ago  | discuss</td>\n",
502 |        "    </tr>\n",
503 |        "    <tr>\n",
504 |        "      <th>176</th>\n",
505 |        "      <td>Rodeo 1.3 - Tab-completion for docstrings</td>\n",
506 |        "      <td>3 points by glamp 35 days ago  | discuss</td>\n",
507 |        "    </tr>\n",
508 |        "    <tr>\n",
509 |        "      <th>177</th>\n",
510 |        "      <td>Teaching D3.js - links</td>\n",
511 |        "      <td>3 points by pmigdal 35 days ago  | discuss</td>\n",
512 |        "    </tr>\n",
513 |        "    <tr>\n",
514 |        "      <th>178</th>\n",
515 |        "      <td>Parallel scikit-learn on YARN</td>\n",
516 |        "      <td>5 points by stijntonk 39 days ago  | discuss</td>\n",
517 |        "    </tr>\n",
518 |        "    <tr>\n",
519 |        "      <th>179</th>\n",
520 |        "      <td>Meetup: Free Live Webinar on Prescriptive Anal...</td>\n",
521 |        "      <td>2 points by ann928 32 days ago  | discuss</td>\n",
522 |        "    </tr>\n",
523 |        "  </tbody>\n",
524 |        "</table>\n",
525 |        "</div>"
526 |       ],
527 |       "text/plain": [
528 |        "                                                 title  \\\n",
529 |        "175   Getting Started with Statistics for Data Science   \n",
530 |        "176          Rodeo 1.3 - Tab-completion for docstrings   \n",
531 |        "177                             Teaching D3.js - links   \n",
532 |        "178                      Parallel scikit-learn on YARN   \n",
533 |        "179  Meetup: Free Live Webinar on Prescriptive Anal...   \n",
534 |        "\n",
535 |        "                                             date  \n",
536 |        "175  3 points by nickhould 35 days ago  | discuss  \n",
537 |        "176      3 points by glamp 35 days ago  | discuss  \n",
538 |        "177    3 points by pmigdal 35 days ago  | discuss  \n",
539 |        "178  5 points by stijntonk 39 days ago  | discuss  \n",
540 |        "179     2 points by ann928 32 days ago  | discuss  "
541 |       ]
542 |      },
543 |      "execution_count": 48,
544 |      "metadata": {},
545 |      "output_type": "execute_result"
546 |     }
547 |    ],
548 |    "source": [
549 |     "df.tail()"
550 |    ]
551 |   },
552 |   {
553 |    "cell_type": "code",
554 |    "execution_count": 49,
555 |    "metadata": {
556 |     "collapsed": true
557 |    },
558 |    "outputs": [],
559 |    "source": [
560 |     "date_string = df['date'][0]"
561 |    ]
562 |   },
563 |   {
564 |    "cell_type": "code",
565 |    "execution_count": 50,
566 |    "metadata": {
567 |     "collapsed": false
568 |    },
569 |    "outputs": [
570 |     {
571 |      "name": "stdout",
572 |      "output_type": "stream",
573 |      "text": [
574 |       "5 points by Rogerh91 6 hours ago  | discuss\n"
575 |      ]
576 |     }
577 |    ],
578 |    "source": [
579 |     "print(date_string)"
580 |    ]
581 |   },
582 |   {
583 |    "cell_type": "code",
584 |    "execution_count": 51,
585 |    "metadata": {
586 |     "collapsed": false
587 |    },
588 |    "outputs": [
589 |     {
590 |      "data": {
591 |       "text/plain": [
592 |        "<_sre.SRE_Match object; span=(23, 28), match='hours'>"
593 |       ]
594 |      },
595 |      "execution_count": 51,
596 |      "metadata": {},
597 |      "output_type": "execute_result"
598 |     }
599 |    ],
600 |    "source": [
601 |     "re.search('hours',date_string)"
602 |    ]
603 |   },
604 |   {
605 |    "cell_type": "code",
606 |    "execution_count": 52,
607 |    "metadata": {
608 |     "collapsed": true
609 |    },
610 |    "outputs": [],
611 |    "source": [
612 |     "date_string = df['date'][50]"
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "code",
617 |    "execution_count": 53,
618 |    "metadata": {
619 |     "collapsed": false
620 |    },
621 |    "outputs": [
622 |     {
623 |      "name": "stdout",
624 |      "output_type": "stream",
625 |      "text": [
626 |       "4 points by lefish 7 days ago  | discuss\n"
627 |      ]
628 |     }
629 |    ],
630 |    "source": [
631 |     "print(date_string)"
632 |    ]
633 |   },
634 |   {
635 |    "cell_type": "code",
636 |    "execution_count": 54,
637 |    "metadata": {
638 |     "collapsed": true
639 |    },
640 |    "outputs": [],
641 |    "source": [
642 |     "# If hours is not there, we don't get any match\n",
643 |     "re.search('hours',date_string)"
644 |    ]
645 |   },
646 |   {
647 |    "cell_type": "code",
648 |    "execution_count": 55,
649 |    "metadata": {
650 |     "collapsed": false
651 |    },
652 |    "outputs": [
653 |     {
654 |      "data": {
655 |       "text/plain": [
656 |        "<_sre.SRE_Match object; span=(19, 24), match='7 day'>"
657 |       ]
658 |      },
659 |      "execution_count": 55,
660 |      "metadata": {},
661 |      "output_type": "execute_result"
662 |     }
663 |    ],
664 |    "source": [
665 |     "# Let us match the digit preceding the day text\n",
666 |     "day_search = re.search('\\d+ day',date_string)\n",
667 |     "day_search"
668 |    ]
669 |   },
670 |   {
671 |    "cell_type": "code",
672 |    "execution_count": 56,
673 |    "metadata": {
674 |     "collapsed": false
675 |    },
676 |    "outputs": [
677 |     {
678 |      "data": {
679 |       "text/plain": [
680 |        "'7 day'"
681 |       ]
682 |      },
683 |      "execution_count": 56,
684 |      "metadata": {},
685 |      "output_type": "execute_result"
686 |     }
687 |    ],
688 |    "source": [
689 |     "days_string = day_search.group(0)\n",
690 |     "days_string"
691 |    ]
692 |   },
693 |   {
694 |    "cell_type": "code",
695 |    "execution_count": 57,
696 |    "metadata": {
697 |     "collapsed": false
698 |    },
699 |    "outputs": [
700 |     {
701 |      "data": {
702 |       "text/plain": [
703 |        "'7'"
704 |       ]
705 |      },
706 |      "execution_count": 57,
707 |      "metadata": {},
708 |      "output_type": "execute_result"
709 |     }
710 |    ],
711 |    "source": [
712 |     "days = days_string.split(' ')[0] \n",
713 |     "days"
714 |    ]
715 |   },
716 |   {
717 |    "cell_type": "markdown",
718 |    "metadata": {},
719 |    "source": []
720 |   },
721 |   {
722 |    "cell_type": "code",
723 |    "execution_count": 58,
724 |    "metadata": {
725 |     "collapsed": true
726 |    },
727 |    "outputs": [],
728 |    "source": [
729 |     "def return_reg_ex_days(row):\n",
730 |     "    days = ''\n",
731 |     "    if re.search('hours',row['date']) is not None:\n",
732 |     "        # print('hours',row['date'])\n",
733 |     "        days = 1\n",
734 |     "    else:\n",
735 |     "        day_search = re.search('\\d+ day',row['date'])\n",
736 |     "        # print('day',day_search.group(0))\n",
737 |     "        days = day_search.group(0).split(' ')[0]    \n",
738 |     "    \n",
739 |     "    #print(row,days)\n",
740 |     "    return days\n",
741 |     "        "
742 |    ]
743 |   },
744 |   {
745 |    "cell_type": "code",
746 |    "execution_count": 59,
747 |    "metadata": {
748 |     "collapsed": false
749 |    },
750 |    "outputs": [],
751 |    "source": [
752 |     "# Now we apply this function to each of the row in the dataframe\n",
753 |     "df['days'] = df.apply(return_reg_ex_days,axis=1)"
754 |    ]
755 |   },
756 |   {
757 |    "cell_type": "code",
758 |    "execution_count": 60,
759 |    "metadata": {
760 |     "collapsed": false
761 |    },
762 |    "outputs": [
763 |     {
764 |      "data": {
765 |       "text/html": [
766 |        "<div>\n",
767 |        "<table border=\"1\" class=\"dataframe\">\n",
768 |        "  <thead>\n",
769 |        "    <tr style=\"text-align: right;\">\n",
770 |        "      <th></th>\n",
771 |        "      <th>title</th>\n",
772 |        "      <th>date</th>\n",
773 |        "      <th>days</th>\n",
774 |        "    </tr>\n",
775 |        "  </thead>\n",
776 |        "  <tbody>\n",
777 |        "    <tr>\n",
778 |        "      <th>0</th>\n",
779 |        "      <td>An Exploration of R, Yelp, and the Search for ...</td>\n",
780 |        "      <td>5 points by Rogerh91 6 hours ago  | discuss</td>\n",
781 |        "      <td>1</td>\n",
782 |        "    </tr>\n",
783 |        "    <tr>\n",
784 |        "      <th>1</th>\n",
785 |        "      <td>Deep Advances in Generative Modeling</td>\n",
786 |        "      <td>7 points by gwulfs 15 hours ago  | 1 comment</td>\n",
787 |        "      <td>1</td>\n",
788 |        "    </tr>\n",
789 |        "    <tr>\n",
790 |        "      <th>2</th>\n",
791 |        "      <td>Spark Pipelines: Elegant Yet Powerful</td>\n",
792 |        "      <td>3 points by aouyang1 9 hours ago  | discuss</td>\n",
793 |        "      <td>1</td>\n",
794 |        "    </tr>\n",
795 |        "    <tr>\n",
796 |        "      <th>3</th>\n",
797 |        "      <td>Shit VCs Say</td>\n",
798 |        "      <td>3 points by Argentum01 10 hours ago  | discuss</td>\n",
799 |        "      <td>1</td>\n",
800 |        "    </tr>\n",
801 |        "    <tr>\n",
802 |        "      <th>4</th>\n",
803 |        "      <td>Python, Machine Learning, and Language Wars</td>\n",
804 |        "      <td>4 points by pmigdal 17 hours ago  | discuss</td>\n",
805 |        "      <td>1</td>\n",
806 |        "    </tr>\n",
807 |        "  </tbody>\n",
808 |        "</table>\n",
809 |        "</div>"
810 |       ],
811 |       "text/plain": [
812 |        "                                               title  \\\n",
813 |        "0  An Exploration of R, Yelp, and the Search for ...   \n",
814 |        "1               Deep Advances in Generative Modeling   \n",
815 |        "2              Spark Pipelines: Elegant Yet Powerful   \n",
816 |        "3                                       Shit VCs Say   \n",
817 |        "4        Python, Machine Learning, and Language Wars   \n",
818 |        "\n",
819 |        "                                             date days  \n",
820 |        "0     5 points by Rogerh91 6 hours ago  | discuss    1  \n",
821 |        "1    7 points by gwulfs 15 hours ago  | 1 comment    1  \n",
822 |        "2     3 points by aouyang1 9 hours ago  | discuss    1  \n",
823 |        "3  3 points by Argentum01 10 hours ago  | discuss    1  \n",
824 |        "4     4 points by pmigdal 17 hours ago  | discuss    1  "
825 |       ]
826 |      },
827 |      "execution_count": 60,
828 |      "metadata": {},
829 |      "output_type": "execute_result"
830 |     }
831 |    ],
832 |    "source": [
833 |     "df.head()"
834 |    ]
835 |   },
836 |   {
837 |    "cell_type": "code",
838 |    "execution_count": 61,
839 |    "metadata": {
840 |     "collapsed": false
841 |    },
842 |    "outputs": [
843 |     {
844 |      "data": {
845 |       "text/html": [
846 |        "<div>\n",
847 |        "<table border=\"1\" class=\"dataframe\">\n",
848 |        "  <thead>\n",
849 |        "    <tr style=\"text-align: right;\">\n",
850 |        "      <th></th>\n",
851 |        "      <th>title</th>\n",
852 |        "      <th>date</th>\n",
853 |        "      <th>days</th>\n",
854 |        "    </tr>\n",
855 |        "  </thead>\n",
856 |        "  <tbody>\n",
857 |        "    <tr>\n",
858 |        "      <th>175</th>\n",
859 |        "      <td>Getting Started with Statistics for Data Science</td>\n",
860 |        "      <td>3 points by nickhould 35 days ago  | discuss</td>\n",
861 |        "      <td>35</td>\n",
862 |        "    </tr>\n",
863 |        "    <tr>\n",
864 |        "      <th>176</th>\n",
865 |        "      <td>Rodeo 1.3 - Tab-completion for docstrings</td>\n",
866 |        "      <td>3 points by glamp 35 days ago  | discuss</td>\n",
867 |        "      <td>35</td>\n",
868 |        "    </tr>\n",
869 |        "    <tr>\n",
870 |        "      <th>177</th>\n",
871 |        "      <td>Teaching D3.js - links</td>\n",
872 |        "      <td>3 points by pmigdal 35 days ago  | discuss</td>\n",
873 |        "      <td>35</td>\n",
874 |        "    </tr>\n",
875 |        "    <tr>\n",
876 |        "      <th>178</th>\n",
877 |        "      <td>Parallel scikit-learn on YARN</td>\n",
878 |        "      <td>5 points by stijntonk 39 days ago  | discuss</td>\n",
879 |        "      <td>39</td>\n",
880 |        "    </tr>\n",
881 |        "    <tr>\n",
882 |        "      <th>179</th>\n",
883 |        "      <td>Meetup: Free Live Webinar on Prescriptive Anal...</td>\n",
884 |        "      <td>2 points by ann928 32 days ago  | discuss</td>\n",
885 |        "      <td>32</td>\n",
886 |        "    </tr>\n",
887 |        "  </tbody>\n",
888 |        "</table>\n",
889 |        "</div>"
890 |       ],
891 |       "text/plain": [
892 |        "                                                 title  \\\n",
893 |        "175   Getting Started with Statistics for Data Science   \n",
894 |        "176          Rodeo 1.3 - Tab-completion for docstrings   \n",
895 |        "177                             Teaching D3.js - links   \n",
896 |        "178                      Parallel scikit-learn on YARN   \n",
897 |        "179  Meetup: Free Live Webinar on Prescriptive Anal...   \n",
898 |        "\n",
899 |        "                                             date days  \n",
900 |        "175  3 points by nickhould 35 days ago  | discuss   35  \n",
901 |        "176      3 points by glamp 35 days ago  | discuss   35  \n",
902 |        "177    3 points by pmigdal 35 days ago  | discuss   35  \n",
903 |        "178  5 points by stijntonk 39 days ago  | discuss   39  \n",
904 |        "179     2 points by ann928 32 days ago  | discuss   32  "
905 |       ]
906 |      },
907 |      "execution_count": 61,
908 |      "metadata": {},
909 |      "output_type": "execute_result"
910 |     }
911 |    ],
912 |    "source": [
913 |     "df.tail()"
914 |    ]
915 |   },
916 |   {
917 |    "cell_type": "code",
918 |    "execution_count": 62,
919 |    "metadata": {
920 |     "collapsed": true
921 |    },
922 |    "outputs": [],
923 |    "source": [
924 |     "# Let us save to a dataframe\n",
925 |     "df.to_csv('data_tau_days.csv', index=False)"
926 |    ]
927 |   }
928 |  ],
929 |  "metadata": {
930 |   "kernelspec": {
931 |    "display_name": "Python 3",
932 |    "language": "python",
933 |    "name": "python3"
934 |   },
935 |   "language_info": {
936 |    "codemirror_mode": {
937 |     "name": "ipython",
938 |     "version": 3
939 |    },
940 |    "file_extension": ".py",
941 |    "mimetype": "text/x-python",
942 |    "name": "python",
943 |    "nbconvert_exporter": "python",
944 |    "pygments_lexer": "ipython3",
945 |    "version": "3.5.1"
946 |   }
947 |  },
948 |  "nbformat": 4,
949 |  "nbformat_minor": 0
950 | }
951 | 


--------------------------------------------------------------------------------
/notebook/twitter/Acquire.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Acquire the Data"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "## Sources of Data\n",
 15 |     "\n",
 16 |     "We want to understand what are the important trends in Machine Learning at the moment. So we want to get a list of articles about Machine Learning that people are talking about. We can do that from many sources, but we decided to pick three sources to do that.\n",
 17 |     "\n",
 18 |     "1. [Reddit.com - Machine Learning](https://www.reddit.com/r/MachineLearning/) - Reddit is a user generated discussion forum where recent articles and topics on Maching Learning are discussed by the community.\n",
 19 |     "\n",
 20 |     "2. [Data Tau](http://www.datatau.com/)- Data Tau is the hacker news for machine learning. Users post articles about latest trends in data science and machine learning and can have discussion arount it.\n",
 21 |     "\n",
 22 |     "3. [Twitter #machinelearning](https://twitter.com/search?q=%23machinelearning&src=typd) - We can also look at Twitter with #machinelearning tags to find the latest articles and post about machine learning that are being discussed in the social media.\n",
 23 |     "\n",
 24 |     "\n",
 25 |     "## Working with Twitter\n",
 26 |     "\n",
 27 |     "Let us get tweets related to demonetization from twitter, using the the Twitter REST Api.\n"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "markdown",
 32 |    "metadata": {},
 33 |    "source": [
 34 |     "## Understand how to connect with Twitter REST Api"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 9,
 40 |    "metadata": {
 41 |     "collapsed": false
 42 |    },
 43 |    "outputs": [],
 44 |    "source": [
 45 |     "import tweepy"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 24,
 51 |    "metadata": {
 52 |     "collapsed": false
 53 |    },
 54 |    "outputs": [],
 55 |    "source": [
 56 |     "consumer_key = 'EgZLCirrTC7ocLS1zykSJl1eG'\n",
 57 |     "consumer_secret = 'V6jnOXNS8i9mwlmJyA3SKKq3S70qQp6C8zJnxVNNu4bLgHeeeR'\n",
 58 |     "access_token = '126065173-O5vGO1nqeHRV5KOGjlCev2kR8bf1JDYEbW9dxCw1'\n",
 59 |     "access_token_secret = '4GnF0auo6tAoIpwiAB2h7W8xTe3yYjxKa0T6M41rrg'"
 60 |    ]
 61 |   },
 62 |   {
 63 |    "cell_type": "code",
 64 |    "execution_count": 25,
 65 |    "metadata": {
 66 |     "collapsed": true
 67 |    },
 68 |    "outputs": [],
 69 |    "source": [
 70 |     "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": 26,
 76 |    "metadata": {
 77 |     "collapsed": true
 78 |    },
 79 |    "outputs": [],
 80 |    "source": [
 81 |     "auth.set_access_token(access_token, access_token_secret)"
 82 |    ]
 83 |   },
 84 |   {
 85 |    "cell_type": "code",
 86 |    "execution_count": 27,
 87 |    "metadata": {
 88 |     "collapsed": false
 89 |    },
 90 |    "outputs": [],
 91 |    "source": [
 92 |     "api = tweepy.API(auth)"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "markdown",
 97 |    "metadata": {},
 98 |    "source": [
 99 |     "### Search twitter for tweets which contain #demonetization"
100 |    ]
101 |   },
102 |   {
103 |    "cell_type": "code",
104 |    "execution_count": 46,
105 |    "metadata": {
106 |     "collapsed": true
107 |    },
108 |    "outputs": [],
109 |    "source": [
110 |     "demonetization_tweets = tweepy.Cursor(api.search, q=\"#demonetization\").items(100)"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "markdown",
115 |    "metadata": {},
116 |    "source": [
117 |     "### Write the data to a csv"
118 |    ]
119 |   },
120 |   {
121 |    "cell_type": "code",
122 |    "execution_count": 47,
123 |    "metadata": {
124 |     "collapsed": true
125 |    },
126 |    "outputs": [],
127 |    "source": [
128 |     "import csv"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 49,
134 |    "metadata": {
135 |     "collapsed": false
136 |    },
137 |    "outputs": [],
138 |    "source": [
139 |     "with open('demonetization.csv', 'w') as outfile:\n",
140 |     "    writer = csv.writer(outfile, delimiter='\\t')\n",
141 |     "    data = []\n",
142 |     "    for tweet in demonetization_tweets:\n",
143 |     "        data.append([tweet.created_at, tweet.text, tweet.user.screen_name])\n",
144 |     "    writer.writerows(data)\n"
145 |    ]
146 |   },
147 |   {
148 |    "cell_type": "code",
149 |    "execution_count": null,
150 |    "metadata": {
151 |     "collapsed": true
152 |    },
153 |    "outputs": [],
154 |    "source": []
155 |   }
156 |  ],
157 |  "metadata": {
158 |   "anaconda-cloud": {},
159 |   "kernelspec": {
160 |    "display_name": "Python [default]",
161 |    "language": "python",
162 |    "name": "python3"
163 |   },
164 |   "language_info": {
165 |    "codemirror_mode": {
166 |     "name": "ipython",
167 |     "version": 3
168 |    },
169 |    "file_extension": ".py",
170 |    "mimetype": "text/x-python",
171 |    "name": "python",
172 |    "nbconvert_exporter": "python",
173 |    "pygments_lexer": "ipython3",
174 |    "version": "3.5.2"
175 |   }
176 |  },
177 |  "nbformat": 4,
178 |  "nbformat_minor": 0
179 | }
180 | 


--------------------------------------------------------------------------------
/notebook/twitter/Refine.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Refine the Data"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 2,
 13 |    "metadata": {
 14 |     "collapsed": true
 15 |    },
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import pandas as pd"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 4,
 24 |    "metadata": {
 25 |     "collapsed": false
 26 |    },
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "df = pd.read_csv('tweets.csv', sep=\"\\t\")"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 5,
 35 |    "metadata": {
 36 |     "collapsed": false
 37 |    },
 38 |    "outputs": [
 39 |     {
 40 |      "data": {
 41 |       "text/html": [
 42 |        "<div>\n",
 43 |        "<table border=\"1\" class=\"dataframe\">\n",
 44 |        "  <thead>\n",
 45 |        "    <tr style=\"text-align: right;\">\n",
 46 |        "      <th></th>\n",
 47 |        "      <th>created_at</th>\n",
 48 |        "      <th>screen_name</th>\n",
 49 |        "      <th>text</th>\n",
 50 |        "    </tr>\n",
 51 |        "  </thead>\n",
 52 |        "  <tbody>\n",
 53 |        "    <tr>\n",
 54 |        "      <th>0</th>\n",
 55 |        "      <td>Fri Nov 18 23:59:58 +0000 2016</td>\n",
 56 |        "      <td>arunprasad72</td>\n",
 57 |        "      <td>RT @Praveen_1singh: First the stone pelting st...</td>\n",
 58 |        "    </tr>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>1</th>\n",
 61 |        "      <td>Fri Nov 18 23:59:49 +0000 2016</td>\n",
 62 |        "      <td>pranavkisu</td>\n",
 63 |        "      <td>RT @NewDelhiTimesIN: Is the #demonetization of...</td>\n",
 64 |        "    </tr>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>2</th>\n",
 67 |        "      <td>Fri Nov 18 23:59:48 +0000 2016</td>\n",
 68 |        "      <td>bablumohan</td>\n",
 69 |        "      <td>RT @scoopwhoopnews: #BREAKING Banks across Ind...</td>\n",
 70 |        "    </tr>\n",
 71 |        "    <tr>\n",
 72 |        "      <th>3</th>\n",
 73 |        "      <td>Fri Nov 18 23:59:37 +0000 2016</td>\n",
 74 |        "      <td>NagrathRob</td>\n",
 75 |        "      <td>RT @DrGPradhan: .@ravishndtv of @ndtv spreadin...</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>4</th>\n",
 79 |        "      <td>Fri Nov 18 23:59:28 +0000 2016</td>\n",
 80 |        "      <td>ManishPrasa</td>\n",
 81 |        "      <td>RT @YesIamSaffron: जब भी #Demonetization व् का...</td>\n",
 82 |        "    </tr>\n",
 83 |        "  </tbody>\n",
 84 |        "</table>\n",
 85 |        "</div>"
 86 |       ],
 87 |       "text/plain": [
 88 |        "                       created_at   screen_name  \\\n",
 89 |        "0  Fri Nov 18 23:59:58 +0000 2016  arunprasad72   \n",
 90 |        "1  Fri Nov 18 23:59:49 +0000 2016    pranavkisu   \n",
 91 |        "2  Fri Nov 18 23:59:48 +0000 2016    bablumohan   \n",
 92 |        "3  Fri Nov 18 23:59:37 +0000 2016    NagrathRob   \n",
 93 |        "4  Fri Nov 18 23:59:28 +0000 2016   ManishPrasa   \n",
 94 |        "\n",
 95 |        "                                                text  \n",
 96 |        "0  RT @Praveen_1singh: First the stone pelting st...  \n",
 97 |        "1  RT @NewDelhiTimesIN: Is the #demonetization of...  \n",
 98 |        "2  RT @scoopwhoopnews: #BREAKING Banks across Ind...  \n",
 99 |        "3  RT @DrGPradhan: .@ravishndtv of @ndtv spreadin...  \n",
100 |        "4  RT @YesIamSaffron: जब भी #Demonetization व् का...  "
101 |       ]
102 |      },
103 |      "execution_count": 5,
104 |      "metadata": {},
105 |      "output_type": "execute_result"
106 |     }
107 |    ],
108 |    "source": [
109 |     "df.head()"
110 |    ]
111 |   },
112 |   {
113 |    "cell_type": "markdown",
114 |    "metadata": {},
115 |    "source": [
116 |     "To get the date of the title - we will need the following algorithm\n",
117 |     "- If the string contains **hours** we can consider it **1 day**\n",
118 |     "- And if the string has **day**, we pick the number preceding the **day**\n",
119 |     "\n",
120 |     "To apply this algorithm, we need to be able to pick these words and digits from a string. For that we will use Regular Expression."
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "markdown",
125 |    "metadata": {},
126 |    "source": [
127 |     "## Introduction to Regular Expression (Regex)\n",
128 |     "\n",
129 |     "Regular expression is a way of selecting text using symbols in a string.\n",
130 |     "\n",
131 |     "Refer to the following links for an interactive playground\n",
132 |     "- [http://regexr.com](http://regexr.com/)\n",
133 |     "- [http://regex101.com/](http://regex101.com/)"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "code",
138 |    "execution_count": 35,
139 |    "metadata": {
140 |     "collapsed": true
141 |    },
142 |    "outputs": [],
143 |    "source": [
144 |     "import re"
145 |    ]
146 |   },
147 |   {
148 |    "cell_type": "code",
149 |    "execution_count": 36,
150 |    "metadata": {
151 |     "collapsed": true
152 |    },
153 |    "outputs": [],
154 |    "source": [
155 |     "test_string = \"Hello world, welcome to 2016.\""
156 |    ]
157 |   },
158 |   {
159 |    "cell_type": "code",
160 |    "execution_count": 37,
161 |    "metadata": {
162 |     "collapsed": false
163 |    },
164 |    "outputs": [],
165 |    "source": [
166 |     "# We can pass the whole string and re.search will give the first occurence of the value\n",
167 |     "# re.search - This function searches for first occurrence of RE pattern within string.\n",
168 |     "a = re.search('Hello world, welcome to 2016',test_string)"
169 |    ]
170 |   },
171 |   {
172 |    "cell_type": "code",
173 |    "execution_count": 38,
174 |    "metadata": {
175 |     "collapsed": false
176 |    },
177 |    "outputs": [
178 |     {
179 |      "data": {
180 |       "text/plain": [
181 |        "<_sre.SRE_Match object; span=(0, 28), match='Hello world, welcome to 2016'>"
182 |       ]
183 |      },
184 |      "execution_count": 38,
185 |      "metadata": {},
186 |      "output_type": "execute_result"
187 |     }
188 |    ],
189 |    "source": [
190 |     "a"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "code",
195 |    "execution_count": 39,
196 |    "metadata": {
197 |     "collapsed": false
198 |    },
199 |    "outputs": [
200 |     {
201 |      "data": {
202 |       "text/plain": [
203 |        "'Hello world, welcome to 2016'"
204 |       ]
205 |      },
206 |      "execution_count": 39,
207 |      "metadata": {},
208 |      "output_type": "execute_result"
209 |     }
210 |    ],
211 |    "source": [
212 |     "a.group()"
213 |    ]
214 |   },
215 |   {
216 |    "cell_type": "code",
217 |    "execution_count": 40,
218 |    "metadata": {
219 |     "collapsed": false
220 |    },
221 |    "outputs": [
222 |     {
223 |      "data": {
224 |       "text/plain": [
225 |        "'H'"
226 |       ]
227 |      },
228 |      "execution_count": 40,
229 |      "metadata": {},
230 |      "output_type": "execute_result"
231 |     }
232 |    ],
233 |    "source": [
234 |     "# Match the first letters in the string\n",
235 |     "a = re.search('.',test_string)\n",
236 |     "a.group()"
237 |    ]
238 |   },
239 |   {
240 |    "cell_type": "code",
241 |    "execution_count": 41,
242 |    "metadata": {
243 |     "collapsed": false
244 |    },
245 |    "outputs": [
246 |     {
247 |      "data": {
248 |       "text/plain": [
249 |        "'Hello world, welcome to 2016.'"
250 |       ]
251 |      },
252 |      "execution_count": 41,
253 |      "metadata": {},
254 |      "output_type": "execute_result"
255 |     }
256 |    ],
257 |    "source": [
258 |     "# Match all the letters in the string\n",
259 |     "a = re.search('.*',test_string)\n",
260 |     "a.group()"
261 |    ]
262 |   },
263 |   {
264 |    "cell_type": "code",
265 |    "execution_count": 42,
266 |    "metadata": {
267 |     "collapsed": false
268 |    },
269 |    "outputs": [
270 |     {
271 |      "name": "stdout",
272 |      "output_type": "stream",
273 |      "text": [
274 |       "<_sre.SRE_Match object; span=(0, 5), match='Hello'>\n"
275 |      ]
276 |     }
277 |    ],
278 |    "source": [
279 |     "a = re.search('Hello',test_string)\n",
280 |     "print(a)"
281 |    ]
282 |   },
283 |   {
284 |    "cell_type": "markdown",
285 |    "metadata": {},
286 |    "source": [
287 |     "** Some basic symbols**\n",
288 |     "\n",
289 |     "**`?`**   \n",
290 |     "\n",
291 |     "The question mark indicates zero or one occurrences of the preceding element. For example, colou?r matches both \"color\" and \"colour\".\n",
292 |     "\n",
293 |     "**`\\*`**\n",
294 |     "\n",
295 |     "The asterisk indicates zero or more occurrences of the preceding element. For example, ab*c matches \"ac\", \"abc\", \"abbc\", \"abbbc\", and so on.\n",
296 |     "\n",
297 |     "**`\\+`**\t\n",
298 |     "The plus sign indicates one or more occurrences of the preceding element. For example, ab+c matches \"abc\", \"abbc\", \"abbbc\", and so on, but not \"ac\".\n"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "code",
303 |    "execution_count": 43,
304 |    "metadata": {
305 |     "collapsed": false
306 |    },
307 |    "outputs": [
308 |     {
309 |      "name": "stdout",
310 |      "output_type": "stream",
311 |      "text": [
312 |       "<_sre.SRE_Match object; span=(0, 2), match='He'>\n"
313 |      ]
314 |     }
315 |    ],
316 |    "source": [
317 |     "a = re.search('\\w.',test_string)\n",
318 |     "print(a)"
319 |    ]
320 |   },
321 |   {
322 |    "cell_type": "code",
323 |    "execution_count": 44,
324 |    "metadata": {
325 |     "collapsed": false
326 |    },
327 |    "outputs": [
328 |     {
329 |      "name": "stdout",
330 |      "output_type": "stream",
331 |      "text": [
332 |       "<_sre.SRE_Match object; span=(0, 5), match='Hello'>\n"
333 |      ]
334 |     }
335 |    ],
336 |    "source": [
337 |     "a = re.search('\\w*',test_string)\n",
338 |     "print(a)"
339 |    ]
340 |   },
341 |   {
342 |    "cell_type": "markdown",
343 |    "metadata": {},
344 |    "source": [
345 |     "### Exercises"
346 |    ]
347 |   },
348 |   {
349 |    "cell_type": "markdown",
350 |    "metadata": {},
351 |    "source": [
352 |     "Write a regex to remove URL link from the tweets"
353 |    ]
354 |   },
355 |   {
356 |    "cell_type": "code",
357 |    "execution_count": 62,
358 |    "metadata": {
359 |     "collapsed": true
360 |    },
361 |    "outputs": [],
362 |    "source": []
363 |   }
364 |  ],
365 |  "metadata": {
366 |   "anaconda-cloud": {},
367 |   "kernelspec": {
368 |    "display_name": "Python [default]",
369 |    "language": "python",
370 |    "name": "python3"
371 |   },
372 |   "language_info": {
373 |    "codemirror_mode": {
374 |     "name": "ipython",
375 |     "version": 3
376 |    },
377 |    "file_extension": ".py",
378 |    "mimetype": "text/x-python",
379 |    "name": "python",
380 |    "nbconvert_exporter": "python",
381 |    "pygments_lexer": "ipython3",
382 |    "version": "3.5.2"
383 |   }
384 |  },
385 |  "nbformat": 4,
386 |  "nbformat_minor": 0
387 | }
388 | 


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