├── githubGroups.md ├── .travis.yml ├── DataScienceMethodology.md ├── LICENSE ├── contributing.md ├── Algorithms.md └── README.md /githubGroups.md: -------------------------------------------------------------------------------- 1 | # Github Groups 2 | - [Berkeley Institute for Data Science](https://github.com/BIDS) 3 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: ruby 2 | rvm: 3 | - 2.2 4 | before_script: 5 | - gem install awesome_bot 6 | script: 7 | - awesome_bot README.md 8 | -------------------------------------------------------------------------------- /DataScienceMethodology.md: -------------------------------------------------------------------------------- 1 | ## Data Science Methodology 2 | 3 | This file is created inspiring by John Rollins's Data Science 4 | Methodology [blog 5 | post](http://www.ibmbigdatahub.com/blog/why-we-need-methodology-data-science) 6 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2014 OkulBilişim 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. -------------------------------------------------------------------------------- /contributing.md: -------------------------------------------------------------------------------- 1 | # Contribution Guidelines 2 | 3 | Please ensure your pull request adheres to the following guidelines: 4 | 5 | - Read [the awesome manifesto](https://github.com/sindresorhus/awesome/blob/master/awesome.md) and ensure your list complies. 6 | - Search previous suggestions before making a new one, as yours may be a duplicate. 7 | - Make sure your list is useful before submitting. That implies it having enough content and every item a good succinct description. 8 | - A link back to this list from yours, so users can discover more lists, would be appreciated. 9 | - Make an individual pull request for each suggestion. 10 | - Titles should be [capitalized](http://grammar.yourdictionary.com/capitalization/rules-for-capitalization-in-titles.html). 11 | - Use the following format: `[List Name](link)` 12 | - Link additions should be added to the bottom of the relevant category. 13 | - New categories or improvements to the existing categorization are welcome. 14 | - Check your spelling and grammar. 15 | - Make sure your text editor is set to remove trailing whitespace. 16 | - The pull request and commit should have a useful title. 17 | 18 | Thank you for your suggestions! 19 | -------------------------------------------------------------------------------- /Algorithms.md: -------------------------------------------------------------------------------- 1 | # Algorithms 2 | These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it. 3 | 4 | ### Supervised Learning 5 | - Regression 6 | - Linear Regression 7 | - Ordinary Least Squares 8 | - Logistic Regression 9 | - Stepwise Regression 10 | - Multivariate Adaptive Regression Splines 11 | - Locally Estimated Scatterplot Smoothing 12 | - Classification 13 | - k-nearest neighbor 14 | - Support Vector Machines 15 | - Decision Trees 16 | - ID3 algorithm 17 | - C4.5 algorithm 18 | - Ensemble Learning 19 | - Boosting 20 | - Bagging 21 | - Random Forest 22 | - AdaBoost 23 | 24 | ### Unsupervised Learning 25 | - Clustering 26 | - Hierchical clustering 27 | - k-means 28 | - Fuzzy clustering 29 | - Mixture models 30 | - Neural Networks 31 | - Self-organizing map 32 | - Adaptive resonance theory 33 | 34 | ### Semi-Supervised Learning 35 | ### Reinforcement Learning 36 | - Q Learning 37 | - SARSA (State-Action-Reward-State-Action) algorithm 38 | - Temporal difference learning 39 | 40 | ### Data Mining Algorithms 41 | * C4.5 42 | * k-Means 43 | * SVM 44 | * Apriori 45 | * EM 46 | * PageRank 47 | * AdaBoost 48 | * kNN 49 | * Naive Bayes 50 | * CART 51 | 52 | **Also,you can find most data mining algorithms in [WEKA](http://www.cs.waikato.ac.nz/ml/weka/) program easily ** 53 | 54 | 55 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Data Science [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 2 | 3 | 4 | *An open source Data Science repository to learn and apply towards solving real world problems.* 5 | 6 | ### Table of contents 7 | 8 | * [Motivation](#motivation) 9 | * [Infographic](#infographic) 10 | * [What is Data Science?](#what-is-data-science) 11 | * [Colleges](#colleges) 12 | * [MOOC's](#moocs) 13 | * [Data Sets ](#data-sets) 14 | * [Bloggers](#bloggers) 15 | * [Facebook Accounts](#facebook-accounts) 16 | * [Twitter Accounts ](#twitter-accounts ) 17 | * [YouTube Videos & Channels](#youtube-videos--channels) 18 | * [Toolboxes - Environment](#toolboxes---environment) 19 | * [Journals, Publications and Magazines](#journals-publications-and-magazines) 20 | * [Presentations](#presentations) 21 | * [Other Awesome Lists](#other-awesome-lists) 22 | * [Comics](#comics) 23 | 24 | 25 | ## Motivation 26 | 27 | *This part is for dummies who are new to Data Science* 28 | 29 | This is a shortcut path to start studying **Data Science**. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?" 30 | 31 | First of all, Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. [Here](http://www.quora.com/Data-Science/What-is-data-science) you can find the biggest question for **Data Science** and hundreds of answers from experts. Our favorite data scientist is [Clare Corthell](https://twitter.com/clarecorthell). She is an expert in data-related systems and a hacker, and has been working on a company as a data scientist. [Clare's blog](http://datasciencemasters.org/). This web site helps you to understand the exact way to study as a professional data scientist. 32 | 33 | Secondly, Our favorite programming language is *Python* nowadays for #DataScience. Python's - [Pandas](http://pandas.pydata.org/) library has full functionality for collecting and analyzing data. We use [Anaconda](https://store.continuum.io/cshop/anaconda/) to play with data and to create applications. 34 | 35 | This is the [Guide](https://github.com/okulbilisim/awesome-datascience/blob/master/DataScience-Life-Cycle.md) to begin a **Data Science** project. 36 | 37 | ## Infographic 38 | 39 | Preview | Description 40 | ------------ | ------------- 41 | [](http://i.imgur.com/AfFMkHe.jpg) | A visual guide to Becoming a Data Scientist in 8 Steps by [DataCamp](https://www.datacamp.com) [(img)](http://i.imgur.com/AfFMkHe.jpg) 42 | [](http://i.imgur.com/FxsL3b8.png) | Mindmap on required skills ([img](http://i.imgur.com/FxsL3b8.png)) 43 | [](http://nirvacana.com/thoughts/wp-content/uploads/2013/07/RoadToDataScientist1.png) | Swami Chandrasekaran made a [Curriculum via Metro map](http://nirvacana.com/thoughts/becoming-a-data-scientist/). 44 | [](http://i.imgur.com/4ZBBvb0.png) | by [@kzawadz](https://twitter.com/kzawadz) via [twitter](https://twitter.com/MktngDistillery/status/538671811991715840), [MarketingDistillery.com](http://www.marketingdistillery.com/2014/11/29/is-data-science-a-buzzword-modern-data-scientist-defined/) 45 | [](http://i.imgur.com/4e705Q4.png) | And a male version, from another article by [MarketingDistillery.com](http://www.marketingdistillery.com/2014/08/30/data-science-skill-set-explained/) 46 | [](http://i.imgur.com/xLY3XZn.jpg) | By [Data Science Central](http://www.datasciencecentral.com/) 47 | [](http://i.imgur.com/aoz1BJy.jpg) | From [this article](http://berkeleysciencereview.com/how-to-become-a-data-scientist-before-you-graduate/) by Berkeley Science Review. 48 | [](http://i.imgur.com/0TydZ4M.png) | Data Science Wars: R vs Python 49 | [](http://i.imgur.com/HnRwlce.png) | How to select statistical or machine learning techniques 50 | [](http://i.imgur.com/uEqMwZa.png) | The Data Science Industry: Who Does What 51 | [](http://i.imgur.com/RsHqY84.png) | Data Science Venn Diagram 52 | [](https://www.springboard.com/blog/wp-content/uploads/2016/03/20160324_springboard_vennDiagram.png) | Different Data Science Skills and Roles from [this article](https://www.springboard.com/blog/data-science-career-paths-different-roles-industry/) by Springboard 53 | 54 | 55 | ## What is Data Science? 56 | 57 | * [What is Data Science @ O'reilly](https://www.oreilly.com/ideas/what-is-data-science) 58 | * [What is Data Science @ Quora](http://www.quora.com/Data-Science/What-is-data-science) 59 | * [The sexiest job of 21st century](http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1) 60 | * [What is data science](http://www.datascientists.net/what-is-data-science) 61 | * [What is a data scientist](http://www.becomingadatascientist.com/2014/02/14/what-is-a-data-scientist/) 62 | * [Wikipedia](http://en.wikipedia.org/wiki/Data_science) 63 | * [a very short history of #datascience](http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/) 64 | * [An Introduction to Data Science, PDF](https://ischool.syr.edu/media/documents/2012/3/DataScienceBook1_1.pdf). 65 | * [Data Science Methodology by John Rollins PhD](http://www.ibmbigdatahub.com/blog/why-we-need-methodology-data-science) 66 | * [A Day in the Life of a Data Scientist by Rutgers University](http://online.rutgers.edu/resources/articles/a-day-in-the-life-of-a-data-scientist/) 67 | 68 | ## COLLEGES 69 | 70 | * [A list of colleges and universities offering degrees in data science.](https://github.com/ryanswanstrom/awesome-datascience-colleges) 71 | * [Data Science Degree @ Berkeley](datascience.berkeley.edu) 72 | * [Data Science Degree @ UVA](https://dsi.virginia.edu/) 73 | * [Data Science Degree @ Wisconsin](http://datasciencedegree.wisconsin.edu/) 74 | * [Master of Information @ Rutgers](http://online.rutgers.edu/master-library-info/) 75 | * [MS in Computer Information Systems @ Boston University](https://cisonline.bu.edu/) 76 | * [MS in Business Analytics @ ASU Online] (http://asuonline.asu.edu/online-degree-programs/graduate/master-science-business-analytics/) 77 | * [Data Science Engineer @ BTH](https://www.bth.se/nyheter/bth-startar-sveriges-forsta-civilingenjorsprogram-inom-data-science/) 78 | 79 | ## MOOC's 80 | 81 | * [Google Making Sense of Data](https://datasense.withgoogle.com/course) 82 | * [Coursera Introduction to Data Science](https://www.coursera.org/course/datasci) 83 | * [Data Science - 9 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specialization/jhudatascience/1?utm_medium=listingPage) 84 | * [Data Mining - 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specialization/datamining) 85 | * [Machine Learning – 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/machine-learning) 86 | * [CS 109 Data Science](http://cs109.org/) 87 | * [Schoolofdata](http://schoolofdata.org/) 88 | * [OpenIntro](http://www.openintro.org/) 89 | * [Data science MOOC](http://datascience.sg/categories/MOOC/) 90 | * [CS 171 Visualization](http://www.cs171.org/#!index.md) 91 | * [Process Mining: Data science in Action](https://www.coursera.org/course/procmin) 92 | * [Oxford Deep Learning](http://www.cs.ox.ac.uk/projects/DeepLearn/) 93 | * [Oxford Deep Learning - video](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) 94 | * [Oxford Machine Learning](http://www.cs.ox.ac.uk/activities/machinelearning/) 95 | * [UBC Machine Learning - video](http://www.cs.ubc.ca/~nando/540-2013/lectures.html) 96 | * [Data Science Specialization](https://github.com/DataScienceSpecialization/courses) 97 | * [Coursera Big Data Specialization] (https://www.coursera.org/specializations/big-data) 98 | * [Data Science and Analytics in Context by Edx](https://www.edx.org/xseries/data-science-analytics-context) 99 | * [Big Data University by IBM](http://bigdatauniversity.com/) 100 | * [Udacity - Deep Learning](https://www.udacity.com/course/deep-learning--ud730) 101 | 102 | 103 | ## Data Sets 104 | 105 | * [Academic Torrents](http://academictorrents.com/) 106 | * [hadoopilluminated.com](http://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html) 107 | * [data.gov](http://catalog.data.gov/dataset) - The home of the U.S. Government's open data 108 | * [United States Census Bureau](http://www.census.gov/) 109 | * [freebase.com](https://www.freebase.com/) 110 | * [usgovxml.com](http://usgovxml.com/) 111 | * [enigma.io](http://enigma.io/) - Navigate the world of public data - Quickly search and analyze billions of public records published by governments, companies and organizations. 112 | * [datahub.io](http://datahub.io/) 113 | * [aws.amazon.com/datasets](http://aws.amazon.com/datasets) 114 | * [databib.org](http://databib.org/) 115 | * [datacite.org](http://www.datacite.org) 116 | * [quandl.com](https://www.quandl.com/) - Get the data you need in the form you want; instant download, API or direct to your app. 117 | * [figshare.com](http://figshare.com/) 118 | * [GeoLite Legacy Downloadable Databases](http://dev.maxmind.com/geoip/legacy/geolite/) 119 | * [Quora's Big Datasets Answer](http://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public) 120 | * [Public Big Data Sets](http://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html) 121 | * [Houston Data Portal](http://data.ohouston.org/) 122 | * [Kaggle Data Sources](https://www.kaggle.com/wiki/DataSources) 123 | * [Kaggle Datasets](https://www.kaggle.com/datasets) 124 | * [A Deep Catalog of Human Genetic Variation](http://www.1000genomes.org/data) 125 | * [A community-curated database of well-known people, places, and things](https://www.freebase.com/) 126 | * [Google Public Data](http://www.google.com/publicdata/directory) 127 | * [World Bank Data](http://data.worldbank.org/) 128 | * [NYC Taxi data](http://nyctaxi.herokuapp.com/) 129 | * [Open Data Philly](http://www.opendataphilly.org/) Connecting people with data for Philadelphia 130 | * [A list of useful sources](http://ahmetkurnaz.net/en/statistical-data-sources/) A blog post includes many data set databases 131 | * [grouplens.org](http://grouplens.org/datasets/) Sample movie (with ratings), book and wiki datasets 132 | * [UC Irvine Machine Learning Repository](http://archive.ics.uci.edu/ml/) - contains data sets good for machine learning 133 | * [research-quality data sets](https://bitly.com/bundles/hmason/1) by [Hilary Mason](https://bitly.com/u/hmason/bundles) 134 | * [National Climatic Data Center - NOAA](http://www.ncdc.noaa.gov/) 135 | * [ClimateData.us](http://www.climatedata.us/) (related: [U.S. Climate Resilience Toolkit](http://toolkit.climate.gov/)) 136 | * [r/datasets](http://www.reddit.com/r/datasets) 137 | * [MapLight](http://maplight.org/data) - provides a variety of data free of charge for uses that are freely available to the general public. Click on a data set below to learn more 138 | * [GHDx](http://ghdx.healthdata.org/catalog) - Institute for Health Metrics and Evaluation - a catalog of health and demographic datasets from around the world and including IHME results 139 | * [St. Louis Federal Reserve Economic Data - FRED](http://research.stlouisfed.org/fred2/) 140 | * [New Zealand Institute of Economic Research – Data1850](https://data1850.nz/) 141 | * [Dept. of Politics @ New York University](http://www.nyu.edu/projects/politicsdatalab/data_classic_sources.html) 142 | * [Open Data Sources](https://github.com/datasciencemasters/data) 143 | * [UNICEF Statistics and Monitoring](http://www.unicef.org/statistics/) 144 | * [UNICEF Data](http://data.unicef.org/) 145 | * [undata](http://data.un.org/) 146 | * [NASA SocioEconomic Data and Applications Center - SEDAC](http://sedac.ciesin.columbia.edu/) 147 | * [The GDELT Project](http://gdeltproject.org/) 148 | * [Sweden, Statistics](http://www.scb.se/en_/) 149 | * [Github free data source list](http://www.datasciencecentral.com/profiles/blogs/great-github-list-of-public-data-sets) 150 | * [StackExchange Data Explorer](http://data.stackexchange.com) - an open source tool for running arbitrary queries against public data from the Stack Exchange network. 151 | * [San Fransisco Government Open Data](https://data.sfgov.org/) 152 | * [IBM Blog abour open data](http://www.datasciencecentral.com/profiles/blogs/the-free-big-data-sources-everyone-should-know) 153 | * [Google Public Data](https://www.google.com/publicdata) 154 | * [Open data Index](http://index.okfn.org/) 155 | 156 | ## Bloggers 157 | 158 | - [Wes McKinney](http://blog.wesmckinney.com/) - Wes McKinney Blog. 159 | - [Matthew Russell](http://miningthesocialweb.com/) - Mining The Social Web. 160 | - [Greg Reda](http://www.gregreda.com/) - Greg Reda Personal Blog 161 | - [Kevin Davenport](http://kldavenport.com/) - Kevin Davenport Personal Blog 162 | - [Julia Evans](http://jvns.ca/) - Recurse Center alumna 163 | - [Hakan Kardas](http://www.cse.unr.edu/~hkardes/) - Personal Web Page 164 | - [Sean J. Taylor](http://seanjtaylor.com/) - Personal Web Page 165 | - [Drew Conway](http://drewconway.com/) - Personal Web Page 166 | - [Hilary Mason](http://www.hilarymason.com/) - Personal Web Page 167 | - [Noah Iliinsky](http://complexdiagrams.com/) - Personal Blog 168 | - [Matt Harrison](http://hairysun.com/) - Personal Blog 169 | - [Data Science Renee](http://www.becomingadatascientist.com/) Documenting my path from "SQL Data Analyst pursuing an Engineering Master's Degree" to "Data Scientist" 170 | - [Vamshi Ambati](http://allthingsds.wordpress.com/) - AllThings Data Sciene 171 | - [Prash Chan](http://www.mdmgeek.com/) - Tech Blog on Master Data Management And Every Buzz Surrounding It 172 | - [Clare Corthell](http://datasciencemasters.org/) - The Open Source Data Science Masters 173 | - [Paul Miller](http://cloudofdata.com/) Based in the UK and working globally, Cloud of Data's consultancy services help clients understand the implications of taking data and more to the Cloud. 174 | - [Data Science London](http://datasciencelondon.org/) Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science. 175 | We are the largest data science community in Europe. 176 | We are more than 3,190 data scientists and data geeks in our community. 177 | - [Datawrangling](http://datawrangling.com/) by Peter Skomoroch. MACHINE LEARNING, DATA MINING, AND MORE 178 | - [John Myles White](http://www.johnmyleswhite.com/) Personal Blog 179 | - [Quora Data Science](http://www.quora.com/Data-Science) - Data Science Questions and Answers from experts 180 | - [Siah](http://openresearch.wordpress.com/) a PhD student at Berkeley 181 | - [Data Science Report](http://blog.starbridgepartners.com/) MDS, Inc. Helps Build Careers in Data Science, Advanced Analytics, Big Data Architecture, and High Performance Software Engineering 182 | - [Louis Dorard](http://www.louisdorard.com/blog/) a technology guy with a penchant for the web and for data, big and small 183 | - [Machine Learning Mastery](http://machinelearningmastery.com/) about helping professional programmers to confidently apply machine learning algorithms to address complex problems. 184 | - [Daniel Forsyth](http://www.danielforsyth.me/) - Personal Blog 185 | - [Data Science Weekly](http://www.datascienceweekly.org/) - Weekly News Blog 186 | - [Revolution Analytics](http://blog.revolutionanalytics.com/) - Data Science Blog 187 | - [R Bloggers](http://www.r-bloggers.com/) - R Bloggers 188 | - [The Practical Quant](http://practicalquant.blogspot.com/) Big data 189 | - [Micheal Le Gal](http://www.mickaellegal.com/) a data enthusiast who gets hooked on solving intriguing problems and crafting beautiful stories and visualizations with data. Over the past 5 years, He haas applied statistics to solve problems in government, brain sciences, and most recently, retail. 190 | - [Datascope Anayltics](http://datascopeanalytics.com/) data-driven consulting and design 191 | - [Yet Another Data Blog](http://yet-another-data-blog.blogspot.com.tr/) Yet Another Data Blog 192 | - [Spenczar](http://spenczar.com/) a data scientist at _Twitch_. I handle the whole data pipeline, from tracking to model-building to reporting. 193 | - [KD Nuggets](http://www.kdnuggets.com/) Data Mining, Analytics, Big Data, Data, Science not a blog a portal 194 | - [Meta Brown](http://www.metabrown.com/blog/) - Personal Blog 195 | - [Data Scientist](http://www.datascientists.net/) is building the data scientist culture. 196 | - [WhatSTheBigData](http://whatsthebigdata.com/) is some of, all of, or much more than the above and this blog explores its impact on information technology, the business world, government agencies, and our lives. 197 | - [Mic Farris](http://www.micfarris.com/) Focusing on science, datascience, business, technology, and channeling inner geekness! 198 | - [Tevfik Kosar](http://magnus-notitia.blogspot.com.tr/) - Magnus Notitia 199 | - [New Data Scientist](http://newdatascientist.blogspot.com/) How a Social Scientist Jumps into the World of Big Data 200 | - [Harvard Data Science](http://harvarddatascience.com/) - Thoughts on Statistical Computing and Visualization 201 | - [Data Science 101](http://101.datascience.community/) - Learning To Be A Data Scientist 202 | - [Kaggle Past Solutions](http://www.chioka.in/kaggle-competition-solutions/) 203 | - [DataScientistJourney](http://datascientistjourney.wordpress.com/category/data-science/) 204 | - [NYC Taxi Visualization Blog](http://nyctaxi.herokuapp.com/) 205 | - [Learning Lover](http://learninglover.com/blog/) 206 | - [Huge Trello List of Great Data Science Resources](http://getprismatic.com/story/1406683266166?utm_medium=email) 207 | - [Dataists](http://www.dataists.com/) 208 | - [Data-Mania](http://www.data-mania.com/) 209 | - [Data-Magnum](http://data-magnum.com/) 210 | - [Map Reduce Blog](https://www.mapr.com/blog) 211 | - [FastML Blog](http://fastml.com/) 212 | - [P-value](http://www.p-value.info/) - Musings on data science, machine learning and stats. 213 | - [datascopeanalytics](http://datascopeanalytics.com/what-we-think/) 214 | - [Digital transformation](http://tarrysingh.com/) 215 | - [datascientistjourney](http://datascientistjourney.wordpress.com/category/data-science/) 216 | - [Data Mania Blog](http://www.data-mania.com/index.php/easyblog) 217 | - [The File Drawer](http://filedrawer.wordpress.com/) - Chris Said's science blog 218 | - [Emilio Ferrara's web page](http://www.emilio.ferrara.name/) 219 | - [DataNews](http://datanews.tumblr.com/) 220 | - [Reddit TextMining](http://www.reddit.com/r/textdatamining/) 221 | - [Periscopic](http://www.periscopic.com/#/news) 222 | - [Hilary Parker](http://hilaryparker.com/) 223 | - [Data Stories](http://datastori.es/) 224 | - [Data Science Lab](http://datasciencelab.wordpress.com/) 225 | - [Meaning of](http://www.kennybastani.com/) 226 | - [Adventures in Data Land]( http://blog.smola.org) 227 | - [DATA MINERS BLOG](http://blog.data-miners.com/) 228 | - [Dataclysm](http://blog.okcupid.com/) 229 | - [FlowingData](http://flowingdata.com/) - Visualization and Statistics 230 | - [Calculated Risk](http://www.calculatedriskblog.com/) 231 | - [Applied Data Labs](http://www.applieddatalabs.com/) - content and news about data-driven business. 232 | - [O'reilly Learning Blog](https://beta.oreilly.com/learning) 233 | - [Dominodatalab](http://blog.dominodatalab.com/) 234 | - [i am trask](http://iamtrask.github.io/) - A Machine Learning Craftsmanship Blog 235 | - [Vademecum of Practical Data Science](http://datasciencevademecum.wordpress.com/) - Handbook and recipes for data-driven solutions of real-world problems 236 | - [Dataconomy](http://dataconomy.com/) - A blog on the new emerging data economy 237 | - [Springboard](http://springboard.com/blog) - A blog with resources for data science learners 238 | 239 | ## Facebook Accounts 240 | 241 | - [Data](https://www.facebook.com/data) 242 | - [Big Data Scientist](https://www.facebook.com/Bigdatascientist) 243 | - [Data Science 101](https://www.facebook.com/DataScience101) 244 | - [Data Science Day](https://www.facebook.com/DataScienceDay/) 245 | - [Data Science Academy](https://www.facebook.com/nycdatascience) 246 | - [Facebook Data Science Page](https://www.facebook.com/pages/Data-science/431299473579193?ref=br_rs) 247 | - [Data Science London](https://www.facebook.com/pages/Data-Science-London/226174337471513) 248 | - [Data Science Technology and Corporation](https://www.facebook.com/DataScienceTechnologyCorporation?ref=br_rs) 249 | - [Data Science - Closed Group](https://www.facebook.com/groups/1394010454157077/?ref=br_rs) 250 | - [Center for Data Science](https://www.facebook.com/centerdatasciences?ref=br_rs) 251 | - [Big data hadoop NOSQL Hive Hbase](https://www.facebook.com/groups/bigdatahadoop/) 252 | - [Analytics, Data Mining, Predictive Modeling, Artificial Intelligence](https://www.facebook.com/groups/data.analytics/) 253 | - [Big Data Analytics using R](https://www.facebook.com/groups/434352233255448/) 254 | - [Big Data Analytics with R and Hadoop](https://www.facebook.com/groups/rhadoop/) 255 | - [Big Data Learnings](https://www.facebook.com/groups/bigdatalearnings/) 256 | - [Big Data, Data Science, Data Mining & Statistics](https://www.facebook.com/groups/bigdatastatistics/) 257 | - [BigData/Hadoop Expert](https://www.facebook.com/groups/BigDataExpert/) 258 | - [Data Mining / Machine Learning / AI](https://www.facebook.com/groups/machinelearningforum/) 259 | - [Data Mining/Big Data - Social Network Ana](https://www.facebook.com/groups/dataminingsocialnetworks/) 260 | - [Vademecum of Practical Data Science](https://www.facebook.com/datasciencevademecum) 261 | - [Veri Bilimi Istanbul](https://www.facebook.com/groups/veribilimiistanbul/) 262 | - [The Data Science Blog](https://www.facebook.com/theDataScienceBlog/) 263 | 264 | ## Twitter Accounts 265 | 266 | - [Big Data Combine](https://twitter.com/BigDataCombine) - Rapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies 267 | - [Big Data Mania](https://twitter.com/BigDataGal) - Data Viz Wiz | Data Journalist | Growth Hacker | Author of Data Science for Dummies (2015) 268 | - [Big Data Science](https://twitter.com/analyticbridge) - Big Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research. 269 | - [Charlie Greenbacker](https://twitter.com/greenbacker) - Director of Data Science at @ExploreAltamira 270 | - [Chris Said](https://twitter.com/Chris_Said) - Data scientist at Twitter 271 | - [Clare Corthell](https://twitter.com/clarecorthell) - Dev, Design, Data Science @mattermark #hackerei 272 | - [DADI Charles-Abner](https://twitter.com/DadiCharles) - #datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast 273 | - [Data Science Central](https://twitter.com/DataScienceCtrl) - Data Science Central is the industry's single resource for Big Data practitioners. 274 | - [Data Science London](https://twitter.com/ds_ldn) Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data 275 | - [Data Science Renee](https://twitter.com/BecomingDataSci) - Documenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist 276 | - [Data Science Report](https://twitter.com/TedOBrien93) - Mission is to help guide & advance careers in Data Science & Analytics 277 | - [Data Science Tips](https://twitter.com/datasciencetips) - Tips and Tricks for Data Scientists around the world! #datascience #bigdata 278 | - [Data Vizzard](https://twitter.com/DataVisualizati) - DataViz, Security, Military 279 | - [DataScienceX](https://twitter.com/DataScienceX) 280 | - [deeplearning4j](https://twitter.com/deeplearning4j) - 281 | - [DJ Patil](https://twitter.com/dpatil) - White House Data Chief, VP @ RelateIQ. 282 | - [Domino Data Lab](https://twitter.com/DominoDataLab) 283 | - [Drew Conway](https://twitter.com/drewconway) - Data nerd, hacker, student of conflict. 284 | - [Emilio Ferrara](https://twitter.com/jabawack) - #Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv 285 | - [Erin Bartolo](https://twitter.com/erinbartolo) - Running with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr. 286 | - [Greg Reda](https://twitter.com/gjreda) Working @ _GrubHub_ about data and pandas 287 | - [Gregory Piatetsky](https://twitter.com/kdnuggets) - KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher. 288 | - [Gregory Piatetsky](https://twitter.com/kdnuggets) - KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher. 289 | - [Hakan Kardas](https://twitter.com/hakan_kardes) - Data Scientist 290 | - [Hilary Mason](https://twitter.com/hmason) - Data Scientist in Residence at @accel. 291 | - [Jeff Hammerbacher](https://twitter.com/hackingdata) ReTweeting about data science 292 | - [John Myles White](https://twitter.com/johnmyleswhite) Scientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only. 293 | - [Juan Miguel Lavista](https://twitter.com/BDataScientist) - Principal Data Scientist @ Microsoft Data Science Team 294 | - [Julia Evans](https://twitter.com/b0rk) - Hacker - Pandas - Data Analyze 295 | - [Kenneth Cukier](https://twitter.com/kncukier) - The Economist's Data Editor and co-author of Big Data (http://big-data-book.com ). 296 | - [Kevin Davenport](https://twitter.com/KevinLDavenport) - Organizer of http://sddatascience.com 297 | - [Kim Rees](https://twitter.com/krees) - Interactive data visualization and tools. Data flaneur. 298 | - [Kirk Borne](https://twitter.com/KirkDBorne) - DataScientist, PhD Astrophysicist, Top #BigData Influencer. 299 | - [Linda Regber](https://twitter.com/LindaRegber) - Data story teller, visualizations. 300 | - [Luis Rei](https://twitter.com/lmrei) - PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science. 301 | - [Machine Learning](https://twitter.com/ML_toparticles) - Live Content Curated by top 1K Machine Learning Experts 302 | - [Mark Stevenson](https://twitter.com/Agent_Analytics) - Data Analytics Recruitment Specialist at Salt (@SaltJobs) | Analytics - Insight - Big Data - Datascience 303 | - [Matt Harrison](https://twitter.com/__mharrison__) - Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, ult|goalt-imate, organic gardening. 304 | - [Matthew Russell](https://twitter.com/ptwobrussell) - Mining the Social Web. 305 | - [Mert Nuhoğlu](https://twitter.com/mertnuhoglu) Data Scientist at BizQualify, Developer 306 | - [Monica Rogati](https://twitter.com/mrogati) - Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer. 307 | - [Noah Iliinsky](https://twitter.com/noahi) - Visualization & interaction designer. Practical cyclist. Author of vis books: http://www.oreillynet.com/pub/au/4419 308 | - [Paul Miller](https://twitter.com/PaulMiller) - Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst. 309 | - [Peter Skomoroch](https://twitter.com/peteskomoroch) - Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks 310 | - [Prash Chan](https://twitter.com/MDMGeek) - Solution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud. 311 | - [Quora Data Science](https://twitter.com/q_datascience) Quora's data science topic 312 | - [R-Bloggers](https://twitter.com/Rbloggers) - Tweet blog posts from the R blogosphere, data science conferences and (!) open jobs for data scientists. 313 | - [Rand Hindi](https://twitter.com/randhindi) 314 | - [Randy Olson](https://twitter.com/randal_olson) - Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate. 315 | - [Recep Erol](https://twitter.com/EROLRecep) - Data Science geek @ UALR 316 | - [Ryan Orban](https://twitter.com/ryanorban) - Data scientist, genetic origamist, hardware aficionado 317 | - [Sean J. Taylor](https://twitter.com/seanjtaylor) - Social Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics. 318 | - [Silvia K. Spiva](https://twitter.com/silviakspiva) - #DataScience at Cisco 319 | - [Spencer Nelson](https://twitter.com/spenczar_n) - Data nerd 320 | - [Talha Oz](https://twitter.com/tozCSS) - Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile kaggler/data scientist 321 | - [Tasos Skarlatidis](https://twitter.com/anskarl) - Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source. 322 | - [Terry Timko](https://twitter.com/Terry_Timko) - InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence 323 | - [TextDataMiningReddit](https://twitter.com/TextMining_r) 324 | - [Tony Baer](https://twitter.com/TonyBaer) - IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in. 325 | - [Tony Ojeda](https://twitter.com/tonyojeda3) - Data Scientist | Author | Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC 326 | - [Vamshi Ambati](https://twitter.com/vambati) - Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: http://allthingsds.wordpress.com ) 327 | - [Wes McKinney](https://twitter.com/wesmckinn) - Pandas (Python Data Analysis library). 328 | - [WileyEd](https://twitter.com/WileyEd) - Senior Manager - @Seagate Big Data Analytics | @McKinsey Alum | #BigData + #Analytics Evangelist | #Hadoop, #Cloud, #Digital, & #R Enthusiast 329 | - [WNYC Data News Team](https://twitter.com/datanews) - The data news crew at @WNYC. Practicing data-driven journalism, making it visual and showing our work. 330 | @SkymindIO's open-source deep learning for the JVM. Integrates with Hadoop, Spark. Distributed GPU/CPUs | http://nd4j.org | http://www.skymind.io 331 | 332 | ## Youtube Videos & Channels 333 | 334 | - [What is machine learning?](https://www.youtube.com/watch?v=WXHM_i-fgGo) 335 | - [Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) 336 | - [Deep Learning: Intelligence from Big Data](https://www.youtube.com/watch?v=czLI3oLDe8M) 337 | - [Interview with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton](https://www.youtube.com/watch?v=1Wp3IIpssEc) 338 | - [Introduction to Deep Learning with Python](https://www.youtube.com/watch?v=S75EdAcXHKk) 339 | - [What is machine learning, and how does it work?](https://www.youtube.com/watch?v=elojMnjn4kk) 340 | - [Data School](https://www.youtube.com/channel/UCnVzApLJE2ljPZSeQylSEyg) - Data Science Education 341 | - [Neural Nets for Newbies by Melanie Warrick (May 2015)](https://www.youtube.com/watch?v=Cu6A96TUy_o) 342 | - [Google DeepMind co-founder Shane Legg - Machine Super Intelligence](https://www.youtube.com/watch?v=evNCyRL3DOU) 343 | 344 | 345 | ## Toolboxes - Environment 346 | * [Datalab from Google](https://cloud.google.com/datalab/overview) easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively. 347 | * [Hortonworks Sandbox](http://hortonworks.com/products/hortonworks-sandbox/) is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials. 348 | * [R](http://www.r-project.org/) is a free software environment for statistical computing and graphics. 349 | * [RStudio](https://www.rstudio.com) IDE – powerful user interface for R. It’s free and open source, works onWindows, Mac, and Linux. 350 | * [Python - Pandas - Anaconda](https://store.continuum.io/cshop/anaconda/) Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing 351 | * [Scikit-Learn](http://scikit-learn.org/stable/) Machine Learning in Python 352 | * [Data Science Toolbox](https://www.coursera.org/course/datascitoolbox) - Coursera Course 353 | * [Data Science Toolbox](http://datasciencetoolbox.org/) - Blog 354 | * [Wolfram Data Science Platform](http://www.wolfram.com/data-science-platform/) Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generating rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language. 355 | * [Sense Data Science Development Paltform](https://senseplatform.com/) A New Cloud Platform for Data Science and Big Data Analytics 356 | Collaborate on, scale, and deploy data analysis and advanced analytics projects radically faster. Use the most powerful tools — R, Python, JavaScript, Redshift, Hive, Impala, Hadoop, and more — supercharged and integrated in the cloud. 357 | * [Mortardata](http://www.mortardata.com/) Solutions, code, and devops for high-scale data science. 358 | * [Variance](https://variancecharts.com/) Build powerful data visualizations for the web without writing JavaScript 359 | * [Kite Development Kit](http://kitesdk.org/docs/current/index.html) The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem. 360 | * [Domino Data Labs](http://www.dominoup.com/) Run, scale, share, and deploy your models — without any infrastructure or setup. 361 | * [Apache Flink](https://flink.incubator.apache.org/) A platform for efficient, distributed, general-purpose data processing. 362 | * [Apache Hama](http://hama.apache.org/) Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. 363 | * [Weka](http://www.cs.waikato.ac.nz/ml/weka/) Weka is a collection of machine learning algorithms for data mining tasks. 364 | * [Octave](https://www.gnu.org/software/octave/) GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab) 365 | * [Apache Spark](https://spark.apache.org/) Lightning-fast cluster computing 366 | * [Caffe](http://caffe.berkeleyvision.org/) Deep Learning Framework 367 | * [Torch](http://torch.ch/) A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT 368 | * [Nervana's python based Deep Learning Framework](https://github.com/NervanaSystems/neon) 369 | * [Aerosolve](http://airbnb.io/aerosolve/) - A machine learning package built for humans. 370 | * [Intel framework](https://github.com/01org/idlf) - Intel® Deep Learning Framework 371 | * [Datawrapper](https://datawrapper.de) – An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at [github.com](https://github.com/datawrapper/datawrapper) 372 | * [Tensor Flow](https://www.tensorflow.org/) - TensorFlow is an Open Source Software Library for Machine Intelligence 373 | * [Natural Language Toolkit](http://www.nltk.org/) 374 | * [nlp-toolkit for node.js](https://www.npmjs.com/package/nlp-toolkit) 375 | * [Julia](http://julialang.org) – high-level, high-performance dynamic programming language for technical computing. 376 | * [IJulia](https://github.com/JuliaLang/IJulia.jl) – a Julia-language backend combined with the Jupyter interactive environment. 377 | 378 | 379 | ## Visualization Tools - Environments 380 | 381 | * [addepar](http://addepar.github.io/#/ember-charts/overview) 382 | * [amcharts](http://www.amcharts.com/) 383 | * [anychart](http://www.anychart.com/home/) 384 | * [capsidea](https://capsidea.com/) 385 | * [cartodb](http://cartodb.github.io/odyssey.js/) 386 | * [Cube](http://square.github.io/cube/) 387 | * [d3plus](http://d3plus.org/) 388 | * [Data-Driven Documents(D3js)](http://d3js.org/) 389 | * [datahero](https://datahero.com/) 390 | * [dygraphs](http://dygraphs.com/) 391 | * [ECharts](http://echarts.baidu.com/index-en.html) 392 | * [exhibit](http://www.simile-widgets.org/exhibit/) 393 | * [Gatherplot](http://www.gatherplot.org/) 394 | * [gephi](http://gephi.github.io/) 395 | * [ggplot2](http://ggplot2.org/) 396 | * [Glue](http://www.glueviz.org/en/latest/) 397 | * [Google Chart Gallery](https://developers.google.com/chart/interactive/docs/gallery) 398 | * [highcarts](http://www.highcharts.com/) 399 | * [import.io](https://import.io/) 400 | * [jqplot](http://www.jqplot.com/) 401 | * [Matplotlib](http://matplotlib.org/) 402 | * [nvd3](http://nvd3.org/) 403 | * [Opendata-tools](http://opendata-tools.org/en/visualization/) - list of open source data visualization tools 404 | * [Openrefine](http://openrefine.org/) 405 | * [plot.ly](https://plot.ly/) 406 | * [raw](http://raw.densitydesign.org/) 407 | * [rcharts](http://rcharts.io/) 408 | * [techanjs](http://techanjs.org/) 409 | * [tenxer](http://tenxer.github.io/xcharts/) 410 | * [Timeline](http://timeline.knightlab.com/) 411 | * [variancecharts](https://variancecharts.com/index.html) 412 | * [vida](https://vida.io/) 413 | * [Wolframalpha](http://www.wolframalpha.com/) 414 | * [Wrangler](http://vis.stanford.edu/wrangler/) 415 | * [r2d3](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) 416 | * [NetworkX](https://networkx.github.io/) - High-productivity software for complex networks 417 | 418 | 419 | ## Journals, Publications and Magazines 420 | 421 | * [ICML](http://icml.cc/2015/) - International Conference on Machine Learning 422 | * [epjdatascience](http://www.epjdatascience.com/) 423 | * [Journal of Data Science](http://www.jds-online.com/) - an international journal devoted to applications of statistical methods at large 424 | * [Big Data Research](http://www.journals.elsevier.com/big-data-research) 425 | * [Journal of Big Data](http://www.journalofbigdata.com/) 426 | * [Big Data & Society](http://bds.sagepub.com/) 427 | * [Data Science Journal](https://www.jstage.jst.go.jp/browse/dsj) 428 | 429 | 430 | ## Presentations 431 | 432 | * [How to Become a Data Scientist](http://www.slideshare.net/ryanorban/how-to-become-a-data-scientist) 433 | * [Introduction to Data Science](http://www.slideshare.net/NikoVuokko/introduction-to-data-science-25391618) 434 | * [Intro to Data Science for Enterprise Big Data](http://www.slideshare.net/pacoid/intro-to-data-science-for-enterprise-big-data) 435 | * [How to Interview a Data Scientist](http://www.slideshare.net/dtunkelang/how-to-interview-a-data-scientist) 436 | * [How to Share Data with a Statistician](https://github.com/jtleek/datasharing) 437 | * [The Science of a Great Career in Data Science](http://www.slideshare.net/katemats/the-science-of-a-great-career-in-data-science) 438 | * [What Does a Data Scientist Do?](http://www.slideshare.net/datasciencelondon/big-data-sorry-data-science-what-does-a-data-scientist-do) 439 | * [Building Data Start-Ups: Fast, Big, and Focused](http://www.slideshare.net/medriscoll/driscoll-strata-buildingdatastartups25may2011clean) 440 | * [How to win data science competitions with Deep Learning](http://www.slideshare.net/0xdata/how-to-win-data-science-competitions-with-deep-learning) 441 | 442 | ## Comics 443 | * [](Digital Data) 444 | 445 | ## Other Awesome Lists 446 | 447 | - Other amazingly awesome lists can be found in the [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness) list. 448 | - [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) A curated list of awesome Machine Learning frameworks, libraries and software. 449 | - [lists](https://github.com/jnv/lists) 450 | - [awesome-dataviz](https://github.com/fasouto/awesome-dataviz) 451 | - [awesome-python](https://github.com/vinta/awesome-python) 452 | - [Data Science IPython Notebooks.](https://github.com/donnemartin/data-science-ipython-notebooks) 453 | - [awesome-r](https://github.com/qinwf/awesome-R) 454 | - [awesome-datasets](https://github.com/caesar0301/awesome-public-datasets). – An awesome list of high-quality open datasets in public domains 455 | - [awesome-Machine Learning & Deep Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md) 456 | --------------------------------------------------------------------------------