├── .gitignore ├── LICENSE ├── README.md └── contributors.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | *.egg-info/ 23 | .installed.cfg 24 | *.egg 25 | 26 | # PyInstaller 27 | # Usually these files are written by a python script from a template 28 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 29 | *.manifest 30 | *.spec 31 | 32 | # Installer logs 33 | pip-log.txt 34 | pip-delete-this-directory.txt 35 | 36 | # Unit test / coverage reports 37 | htmlcov/ 38 | .tox/ 39 | .coverage 40 | .coverage.* 41 | .cache 42 | nosetests.xml 43 | coverage.xml 44 | *,cover 45 | 46 | # Translations 47 | *.mo 48 | *.pot 49 | 50 | # Django stuff: 51 | *.log 52 | 53 | # Sphinx documentation 54 | docs/_build/ 55 | 56 | # PyBuilder 57 | target/ 58 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2015 kmonsoor 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 | 23 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Must-watch videos on Data Science 2 | 3 | This list tries to cover from basic introductory staffs to deep analytical staffs on industry-renowned tools. 4 | 5 | Inspired by [py-must-watch](https://github.com/s16h/py-must-watch). 6 | 7 | ### Contribution 8 | Please create pull requests to suggest more awesome video links. However, please don't add too many in a single pull-request. :) 9 | Please add your name in the file **contributors.md** file, if possible, in the same pull-request you make to add new video in the main list. 10 | 11 | Thanks ! 12 | 13 | ------------------------------------------------------ 14 | 15 | ## Intro into Data-science 16 | * DJ Patil: **Data Science, how to be data driven and build great products** (BBVA Innovation Center, 2014) 17 | * Video: [youtube](https://www.youtube.com/watch?v=54t7bSXniAs) [1:22:37] 18 | * Comments: A great intro. Potrays Spock(Star Trek) as ship's data scientist. 19 | 20 | * Micheael Manoochehri: **Data Just Right: A Practical Introduction to Data Science Skills** (DataEDGE 2013) 21 | * Video: [youtube](https://www.youtube.com/watch?v=rpwZ_i-9U0o) [1:24:50] 22 | * Slides: [Google Docs](http://goo.gl/sCmF0) 23 | * Comments: awesome intro 24 | 25 | * Ryan Orban: **How To Become A Data Scientist** (SF Data Science) 26 | * Video: [youtube](https://www.youtube.com/watch?v=c52IOlnPw08) [59:07] 27 | * Slides: [slideshare](http://www.slideshare.net/ryanorban/how-to-become-a-data-scientist) 28 | * Comments: Audio is a bit hazy with background noise 29 | 30 | * Buck Woody: **Becoming a Data Professional: Taking It To The Next Level** (The DRIVE/conference 2013) 31 | * Video: [youtube](https://www.youtube.com/watch?v=Zdh3p4EKLeQ) [57:05] 32 | * Notes: [MSDN Blog](http://blogs.msdn.com/b/buckwoody/archive/2013/02/21/link-list-becoming-a-data-professional.aspx) 33 | * Comments: 34 | 35 | 36 | ## Machine Learning 37 | * Dr. Andrew Ng: **CS 229: Machine Learning** (Stanford Lecture series 2008) 38 | * Video: [youtube](https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599) (20 videos, 1+Hr each) 39 | * Lecture materials: [Handouts](http://cs229.stanford.edu/materials.html), [course-home](http://cs229.stanford.edu/) 40 | * [Dr. Yaser S. Abu-Mostafa](https://work.caltech.edu/index.html): **Learning from Data** (from Caltech) 41 | * Lecture materials: [Handouts/Videos/Homeworks](https://work.caltech.edu/telecourse.html) 42 | * Comments: A self-paced advanced course on Machine Learning 43 | * Jake VanderPlas: Machine Learning with Scikit-Learn (I) (PyCon 2015) 44 | * Video: [youtube](https://www.youtube.com/watch?v=L7R4HUQ-eQ0)[3:02:11] 45 | * Lecture materials: [codes](https://github.com/jakevdp/sklearn_pycon2015), [IPython Notebook](http://nbviewer.ipython.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb) 46 | 47 | 48 | ## Statistical Methods 49 | * Chris Fonnesbeck: **Statistical Thinking for Data Science** (SciPy 2015) 50 | * Video: [youtube](https://www.youtube.com/watch?v=TGGGDpb04Yc) [23:49] 51 | 52 | 53 | ## Algorithms 54 | * Presenter-name: **Title of the video** (Event, Year) 55 | * Video: [video link] [length-of-video] 56 | * Slides: [slide link] 57 | * [Misc resources](link) 58 | * 59 | 60 | 61 | 62 | ## Mathematics 63 | * Presenter-name: **Title of the video** (Event, Year) 64 | * Video: [video link] [length-of-video] 65 | * Slides: [slide link] 66 | * [Misc resources](link) 67 | * 68 | 69 | 70 | 71 | ## Intro on Hadoop Ecosystem 72 | * Bill Graham: **Intro to Hadoop** (Part of BerkeleyiSchool course Info290:"Analyzing Big Data With Twitter", 2012) 73 | * Video: [youtube](https://www.youtube.com/watch?v=t3fEGhE-HYA) [1:22:26] 74 | * Slides: [presentation](http://blogs.ischool.berkeley.edu/i290-abdt-s12/files/2012/08/BillGraham_IntroToHadoop_Aug30.pdf) 75 | * 76 | 77 | 78 | ## HDFS 79 | * Andrew Collette: **HDF5 is Eating the World** (SciPy 2015) 80 | * Video: (Youtube)[https://www.youtube.com/watch?v=nddj5OA8LJo] [18:30] 81 | * Slides: 82 | 83 | * Quincey Koziol: **Title of the video** (2014) 84 | * Video: (Youtube)[https://www.youtube.com/watch?v=IN1bqxj4pxE] [1:17:09] 85 | * Slides: (mcs.anl.gov)[http://press3.mcs.anl.gov/computingschool/files/2014/01/QKHDF5-Intro-v2.pdf] 86 | 87 | 88 | ## Spark 89 | * Presenter-name: **Title of the video** (Event, Year) 90 | * Video: [video link] [length-of-video] 91 | * Slides: [slide link] 92 | * [Misc resources](link) 93 | * 94 | 95 | 96 | 97 | ## Python-based analysis 98 | * Wes McKinney: **My Data Journey with Python** (SciPy 2015) 99 | * Video: [youtube](https://www.youtube.com/watch?v=kHdkFyGCxiY) [47:45] 100 | * Slides: [SlideShare](http://www.slideshare.net/wesm/my-data-journey-with-python) 101 | * Sarah Guido: **Hands-on Data Analysis with Python** (PyCon 2015) 102 | * Video: [Youtube](https://www.youtube.com/watch?v=L4Hbv4ugUWk) [2:54:57] 103 | * Jonathan Rocher: **Analyzing and Manipulating Data with Pandas** (SciPy 2015 Tutorial ) 104 | * Video: [Youtube](https://www.youtube.com/watch?v=0CFFTJUZ2dc) 105 | * [Exercise and tutorial instructions](https://github.com/jonathanrocher/pandas_tutorial) 106 | * Andreas Mueller & Kyle Kastner: **Machine Learning with Scikit Learn** (SciPy 2015 Tutorial) 107 | * Video: [Part I](https://www.youtube.com/watch?v=80fZrVMurPM), [Part II](https://www.youtube.com/watch?v=Ud-FsEWegmA) 108 | * [Tutorial Materials](https://github.com/amueller/scipy_2015_sklearn_tutorial) 109 | 110 | ## R-based analysis 111 | * David Langer: **Introduction to Data Science with R - Data Analysis** (2014) 112 | * Video: [https://www.youtube.com/watch?v=32o0DnuRjfg&index=1&list=PLTJTBoU5HOCRrTs3cJK-PbHM39cwCU0PF] [1:21:49] 113 | * Source code: [https://github.com/EasyD/IntroToDataScience] 114 | * This is the first video of a two-part tutorial. 115 | 116 | ## Uncategorized 117 | * Presenter-name: **Title of the video** (Event, Year) 118 | * Video: [video link] [length-of-video] 119 | * Slides: [slide link] 120 | * comments 121 | * 122 | 123 | -------------------------------------------------------------------------------- /contributors.md: -------------------------------------------------------------------------------- 1 | Please add your name and Github profile-link here, if possible, in the same pull-request you make to add new video in the main list. 2 | 3 | 4 | * Khaled Monsoor ([kmonsoor](https://github.com/kmonsoor/)) 5 | * Iftekhar Anam ([iftekharanam](https://github.com/iftekharanam)) 6 | * 7 | --------------------------------------------------------------------------------