├── githubGroups.md ├── .travis.yml ├── DataScienceMethodology.md ├── ToDo-List.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 | -------------------------------------------------------------------------------- /ToDo-List.md: -------------------------------------------------------------------------------- 1 | # We highly encourage you to help us to update this repository. 2 | If you have any idea to add or remove any topic to this list is very welcome. We want to help data science community make them involve to this repository. 3 | 4 | 5 | ## To Do List of this repository 6 | 7 | - [ ] Check dead links in the list. 8 | - [ ] Add **Data Science Competition** topic. 9 | - [ ] Add python notebooks, open source projects. 10 | - [ ] Data Science guide for beginners. 11 | - [ ] Move this repository to a web page. 12 | - [ ] Add **Project Ideas** topic. (If you have an idea and don't know how to do, share your idea here to let others know to make it alive.) 13 | -------------------------------------------------------------------------------- /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 | * [Podcasts](#podcasts) 16 | * [Books](#books) 17 | * [Facebook Accounts](#facebook-accounts) 18 | * [Twitter Accounts ](#twitter-accounts ) 19 | * [YouTube Videos & Channels](#youtube-videos--channels) 20 | * [Toolboxes - Environment](#toolboxes---environment) 21 | * [Journals, Publications and Magazines](#journals-publications-and-magazines) 22 | * [Presentations](#presentations) 23 | * [Data Science Competitions](#competitions) 24 | * [Comics](#comics) 25 | * [Tutorials](#tutorials) 26 | * [Other Awesome Lists](#other-awesome-lists) 27 | 28 | 29 | ## Motivation 30 | 31 | *This part is for dummies who are new to Data Science* 32 | 33 | 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?" 34 | 35 | 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](https://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 website helps you to understand the exact way to study as a professional data scientist. 36 | 37 | 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://www.continuum.io/downloads) to play with data and to create applications. 38 | 39 | ## Infographic 40 | 41 | Preview | Description 42 | ------------ | ------------- 43 | [](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/DataScienceEightSteps_Full.png) | A visual guide to Becoming a Data Scientist in 8 Steps by [DataCamp](https://www.datacamp.com) [(img)](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/DataScienceEightSteps_Full.png) 44 | [](http://i.imgur.com/FxsL3b8.png) | Mindmap on required skills ([img](http://i.imgur.com/FxsL3b8.png)) 45 | [](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/). 46 | [](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/) 47 | [](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/) 48 | [](http://i.imgur.com/xLY3XZn.jpg) | By [Data Science Central](http://www.datasciencecentral.com/) 49 | [](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. 50 | [](http://i.imgur.com/0TydZ4M.png) | Data Science Wars: R vs Python 51 | [](http://i.imgur.com/HnRwlce.png) | How to select statistical or machine learning techniques 52 | [](http://scikit-learn.org/stable/_static/ml_map.png) | Choosing the Right Estimator 53 | [](http://i.imgur.com/uEqMwZa.png) | The Data Science Industry: Who Does What 54 | [](http://i.imgur.com/RsHqY84.png) | Data Science Venn Diagram 55 | [](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 56 | [Data Fallacies To Avoid](https://data-literacy.geckoboard.com/poster/) | A simple and friendly way of teaching your non-data scientist/non-statistician colleagues [how to avoid mistakes with data](https://data-literacy.geckoboard.com/poster/). From Geckoboard's [Data Literacy Lessons](https://data-literacy.geckoboard.com/). 57 | 58 | 59 | ## What is Data Science? 60 | 61 | * [What is Data Science @ O'reilly](https://www.oreilly.com/ideas/what-is-data-science) 62 | * [What is Data Science @ Quora](https://www.quora.com/Data-Science/What-is-data-science) 63 | * [The sexiest job of 21st century](https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century) 64 | * [What is data science](http://www.datascientists.net/what-is-data-science) 65 | * [What is a data scientist](http://www.becomingadatascientist.com/2014/02/14/what-is-a-data-scientist/) 66 | * [Wikipedia](https://en.wikipedia.org/wiki/Data_science) 67 | * [a very short history of #datascience](http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/) 68 | * [An Introduction to Data Science, PDF](https://ischool.syr.edu/media/documents/2012/3/DataScienceBook1_1.pdf). 69 | * [Data Science Methodology by John Rollins PhD](http://www.ibmbigdatahub.com/blog/why-we-need-methodology-data-science) 70 | * [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/) 71 | 72 | ## COLLEGES 73 | 74 | * [A list of colleges and universities offering degrees in data science.](https://github.com/ryanswanstrom/awesome-datascience-colleges) 75 | * [Data Science Degree @ Berkeley](https://datascience.berkeley.edu/) 76 | * [Data Science Degree @ UVA](https://dsi.virginia.edu/) 77 | * [Data Science Degree @ Wisconsin](http://datasciencedegree.wisconsin.edu/) 78 | * [Master of Information @ Rutgers](http://online.rutgers.edu/master-library-info/) 79 | * [MS in Computer Information Systems @ Boston University](http://cisonline.bu.edu/) 80 | * [MS in Business Analytics @ ASU Online](http://asuonline.asu.edu/online-degree-programs/graduate/master-science-business-analytics/) 81 | * [Data Science Engineer @ BTH](https://www.bth.se/nyheter/bth-startar-sveriges-forsta-civilingenjorsprogram-inom-data-science/) 82 | * [MS in Applied Data Science @ Syracuse](https://ischool.syr.edu/academics/graduate/masters-degrees/ms-in-applied-data-science/) 83 | * [M.S. Management & Data Science @ Leuphana](https://www.leuphana.de/en/graduate-school/master/course-offerings/management-data-science.html) 84 | 85 | ## MOOC's 86 | 87 | * [Coursera Introduction to Data Science](https://www.coursera.org/specializations/data-science) 88 | * [Data Science - 9 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/jhu-data-science) 89 | * [Data Mining - 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specialization/datamining) 90 | * [Machine Learning – 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/machine-learning) 91 | * [CS 109 Data Science](http://cs109.github.io/2015/) 92 | * [Schoolofdata](https://schoolofdata.org/) 93 | * [OpenIntro](https://www.openintro.org/) 94 | * [Data science MOOC](http://datascience.sg/categories/MOOC/) 95 | * [CS 171 Visualization](http://www.cs171.org/#!index.md) 96 | * [Process Mining: Data science in Action](https://www.coursera.org/learn/process-mining) 97 | * [Oxford Deep Learning](http://www.cs.ox.ac.uk/projects/DeepLearn/) 98 | * [Oxford Deep Learning - video](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) 99 | * [Oxford Machine Learning](http://www.cs.ox.ac.uk/activities/machinelearning/) 100 | * [UBC Machine Learning - video](http://www.cs.ubc.ca/~nando/540-2013/lectures.html) 101 | * [Data Science Specialization](https://github.com/DataScienceSpecialization/courses) 102 | * [Coursera Big Data Specialization](https://www.coursera.org/specializations/big-data) 103 | * [Data Science and Analytics in Context by Edx](https://www.edx.org/xseries/data-science-analytics-context) 104 | * [Big Data University by IBM](https://bigdatauniversity.com/) 105 | * [Udacity - Deep Learning](https://www.udacity.com/course/deep-learning--ud730) 106 | * [Keras in Motion](https://www.manning.com/livevideo/keras-in-motion) 107 | 108 | 109 | ## Data Sets 110 | 111 | * [Academic Torrents](http://academictorrents.com/) 112 | * [hadoopilluminated.com](http://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html) 113 | * [data.gov](https://catalog.data.gov/dataset) - The home of the U.S. Government's open data 114 | * [United States Census Bureau](http://www.census.gov/) 115 | * [usgovxml.com](http://usgovxml.com/) 116 | * [enigma.com](http://enigma.com/) - Navigate the world of public data - Quickly search and analyze billions of public records published by governments, companies and organizations. 117 | * [datahub.io](https://datahub.io/) 118 | * [aws.amazon.com/datasets](https://aws.amazon.com/datasets/) 119 | * [databib.org](http://databib.org/) 120 | * [datacite.org](https://www.datacite.org) 121 | * [quandl.com](https://www.quandl.com/) - Get the data you need in the form you want; instant download, API or direct to your app. 122 | * [figshare.com](https://figshare.com/) 123 | * [GeoLite Legacy Downloadable Databases](http://dev.maxmind.com/geoip/legacy/geolite/) 124 | * [Quora's Big Datasets Answer](https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public) 125 | * [Public Big Data Sets](http://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html) 126 | * [Houston Data Portal](http://data.ohouston.org/) 127 | * [Kaggle Data Sources](https://www.kaggle.com/wiki/DataSources) 128 | * [Kaggle Datasets](https://www.kaggle.com/datasets) 129 | * [A Deep Catalog of Human Genetic Variation](http://www.internationalgenome.org/data) 130 | * [A community-curated database of well-known people, places, and things](https://developers.google.com/freebase/) 131 | * [Google Public Data](http://www.google.com/publicdata/directory) 132 | * [World Bank Data](http://data.worldbank.org/) 133 | * [NYC Taxi data](http://chriswhong.github.io/nyctaxi/) 134 | * [Open Data Philly](https://www.opendataphilly.org/) Connecting people with data for Philadelphia 135 | * [A list of useful sources](http://ahmetkurnaz.net/en/statistical-data-sources/) A blog post includes many data set databases 136 | * [grouplens.org](https://grouplens.org/datasets/) Sample movie (with ratings), book and wiki datasets 137 | * [UC Irvine Machine Learning Repository](http://archive.ics.uci.edu/ml/) - contains data sets good for machine learning 138 | * [research-quality data sets](http://web.archive.org/web/20150320022752/https://bitly.com/bundles/hmason/1) by [Hilary Mason](http://web.archive.org/web/20150501033715/https://bitly.com/u/hmason/bundles) 139 | * [National Climatic Data Center - NOAA](https://www.ncdc.noaa.gov/) 140 | * [ClimateData.us](http://www.climatedata.us/) (related: [U.S. Climate Resilience Toolkit](https://toolkit.climate.gov/)) 141 | * [r/datasets](https://www.reddit.com/r/datasets/) 142 | * [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 143 | * [GHDx](http://ghdx.healthdata.org/) - Institute for Health Metrics and Evaluation - a catalog of health and demographic datasets from around the world and including IHME results 144 | * [St. Louis Federal Reserve Economic Data - FRED](https://fred.stlouisfed.org/) 145 | * [New Zealand Institute of Economic Research – Data1850](https://data1850.nz/) 146 | * [Dept. of Politics @ New York University](http://www.nyu.edu/projects/politicsdatalab/datasupp_datasources.html) 147 | * [Open Data Sources](https://github.com/datasciencemasters/data) 148 | * [UNICEF Statistics and Monitoring](https://www.unicef.org/statistics/index_24287.html) 149 | * [UNICEF Data](https://data.unicef.org/) 150 | * [undata](http://data.un.org/) 151 | * [NASA SocioEconomic Data and Applications Center - SEDAC](http://sedac.ciesin.columbia.edu/) 152 | * [The GDELT Project](http://gdeltproject.org/) 153 | * [Sweden, Statistics](http://www.scb.se/en/) 154 | * [Github free data source list](http://www.datasciencecentral.com/profiles/blogs/great-github-list-of-public-data-sets) 155 | * [StackExchange Data Explorer](http://data.stackexchange.com) - an open source tool for running arbitrary queries against public data from the Stack Exchange network. 156 | * [San Fransisco Government Open Data](https://data.sfgov.org/) 157 | * [IBM Blog abour open data](http://www.datasciencecentral.com/profiles/blogs/the-free-big-data-sources-everyone-should-know) 158 | * [Open data Index](http://index.okfn.org/) 159 | * [Liver Tumor Segmentation Challenge Dataset](http://www.lits-challenge.com/) 160 | * [Public Git Archive](https://github.com/src-d/datasets/tree/master/PublicGitArchive) 161 | * [GHTorrent](http://ghtorrent.org/) 162 | 163 | ## Bloggers 164 | 165 | - [Wes McKinney](http://wesmckinney.com/archives.html) - Wes McKinney Archives. 166 | - [Matthew Russell](https://miningthesocialweb.com/) - Mining The Social Web. 167 | - [Greg Reda](http://www.gregreda.com/) - Greg Reda Personal Blog 168 | - [Kevin Davenport](http://kldavenport.com/) - Kevin Davenport Personal Blog 169 | - [Julia Evans](http://jvns.ca/) - Recurse Center alumna 170 | - [Hakan Kardas](https://www.cse.unr.edu/~hkardes/) - Personal Web Page 171 | - [Sean J. Taylor](http://seanjtaylor.com/) - Personal Web Page 172 | - [Drew Conway](http://drewconway.com/) - Personal Web Page 173 | - [Hilary Mason](https://hilarymason.com/) - Personal Web Page 174 | - [Noah Iliinsky](http://complexdiagrams.com/) - Personal Blog 175 | - [Matt Harrison](http://hairysun.com/) - Personal Blog 176 | - [Data Science Renee](http://www.becomingadatascientist.com/) Documenting my path from "SQL Data Analyst pursuing an Engineering Master's Degree" to "Data Scientist" 177 | - [Vamshi Ambati](https://allthingsds.wordpress.com/) - AllThings Data Sciene 178 | - [Prash Chan](http://www.mdmgeek.com/) - Tech Blog on Master Data Management And Every Buzz Surrounding It 179 | - [Clare Corthell](http://datasciencemasters.org/) - The Open Source Data Science Masters 180 | - [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. 181 | - [Data Science London](http://datasciencelondon.org/) Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science. 182 | We are the largest data science community in Europe. 183 | We are more than 3,190 data scientists and data geeks in our community. 184 | - [Datawrangling](http://datawrangling.com/) by Peter Skomoroch. MACHINE LEARNING, DATA MINING, AND MORE 185 | - [John Myles White](http://www.johnmyleswhite.com/) Personal Blog 186 | - [Quora Data Science](https://www.quora.com/Data-Science) - Data Science Questions and Answers from experts 187 | - [Siah](https://openresearch.wordpress.com/) a PhD student at Berkeley 188 | - [Data Science Report](http://datasciencereport.com/) MDS, Inc. Helps Build Careers in Data Science, Advanced Analytics, Big Data Architecture, and High Performance Software Engineering 189 | - [Louis Dorard](http://www.louisdorard.com/blog/) a technology guy with a penchant for the web and for data, big and small 190 | - [Machine Learning Mastery](http://machinelearningmastery.com/) about helping professional programmers to confidently apply machine learning algorithms to address complex problems. 191 | - [Daniel Forsyth](http://www.danielforsyth.me/) - Personal Blog 192 | - [Data Science Weekly](https://www.datascienceweekly.org/) - Weekly News Blog 193 | - [Revolution Analytics](http://blog.revolutionanalytics.com/) - Data Science Blog 194 | - [R Bloggers](https://www.r-bloggers.com/) - R Bloggers 195 | - [The Practical Quant](https://practicalquant.blogspot.com/) Big data 196 | - [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. 197 | - [Datascope Analytics](https://datascopeanalytics.com/) data-driven consulting and design 198 | - [Yet Another Data Blog](http://yet-another-data-blog.blogspot.com.tr/) Yet Another Data Blog 199 | - [Spenczar](http://spenczar.com/) a data scientist at _Twitch_. I handle the whole data pipeline, from tracking to model-building to reporting. 200 | - [KD Nuggets](http://www.kdnuggets.com/) Data Mining, Analytics, Big Data, Data, Science not a blog a portal 201 | - [Meta Brown](http://www.metabrown.com/blog/) - Personal Blog 202 | - [Data Scientist](http://www.datascientists.net/) is building the data scientist culture. 203 | - [WhatSTheBigData](https://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. 204 | - [Mic Farris](http://www.micfarris.com/) Focusing on science, datascience, business, technology, and channeling inner geekness! 205 | - [Tevfik Kosar](http://magnus-notitia.blogspot.com.tr/) - Magnus Notitia 206 | - [New Data Scientist](http://newdatascientist.blogspot.com/) How a Social Scientist Jumps into the World of Big Data 207 | - [Harvard Data Science](http://harvarddatascience.com/) - Thoughts on Statistical Computing and Visualization 208 | - [Data Science 101](http://101.datascience.community/) - Learning To Be A Data Scientist 209 | - [Kaggle Past Solutions](http://www.chioka.in/kaggle-competition-solutions/) 210 | - [DataScientistJourney](https://datascientistjourney.wordpress.com/category/data-science/) 211 | - [NYC Taxi Visualization Blog](http://chriswhong.github.io/nyctaxi/) 212 | - [Learning Lover](http://learninglover.com/blog/) 213 | - [Dataists](http://www.dataists.com/) 214 | - [Data-Mania](http://www.data-mania.com/) 215 | - [Data-Magnum](http://data-magnum.com/) 216 | - [Map Reduce Blog](https://www.mapr.com/blog) 217 | - [FastML Blog](http://fastml.com/) 218 | - [P-value](http://www.p-value.info/) - Musings on data science, machine learning and stats. 219 | - [datascopeanalytics](https://datascopeanalytics.com/blog/) 220 | - [Digital transformation](http://tarrysingh.com/) 221 | - [datascientistjourney](https://datascientistjourney.wordpress.com/category/data-science/) 222 | - [Data Mania Blog](http://www.data-mania.com/blog/) 223 | - [The File Drawer](http://chris-said.io/) - Chris Said's science blog 224 | - [Emilio Ferrara's web page](http://www.emilio.ferrara.name/) 225 | - [DataNews](http://datanews.tumblr.com/) 226 | - [Reddit TextMining](https://www.reddit.com/r/textdatamining/) 227 | - [Periscopic](http://www.periscopic.com/#/news) 228 | - [Hilary Parker](https://hilaryparker.com/) 229 | - [Data Stories](http://datastori.es/) 230 | - [Data Science Lab](https://datasciencelab.wordpress.com/) 231 | - [Meaning of](http://www.kennybastani.com/) 232 | - [Adventures in Data Land]( http://blog.smola.org) 233 | - [DATA MINERS BLOG](http://blog.data-miners.com/) 234 | - [Dataclysm](https://theblog.okcupid.com/) 235 | - [FlowingData](http://flowingdata.com/) - Visualization and Statistics 236 | - [Calculated Risk](http://www.calculatedriskblog.com/) 237 | - [O'reilly Learning Blog](https://www.oreilly.com/learning) 238 | - [Dominodatalab](https://blog.dominodatalab.com/) 239 | - [i am trask](http://iamtrask.github.io/) - A Machine Learning Craftsmanship Blog 240 | - [Vademecum of Practical Data Science](https://datasciencevademecum.wordpress.com/) - Handbook and recipes for data-driven solutions of real-world problems 241 | - [Dataconomy](http://dataconomy.com/) - A blog on the new emerging data economy 242 | - [Springboard](https://springboard.com/blog) - A blog with resources for data science learners 243 | - [Analytics Vidhya](https://www.analyticsvidhya.com/) - A full-fledged website about data science and analytics study material. 244 | - [Occam's Razor](https://www.kaushik.net/avinash/) - Focused on Web Analytics. 245 | - [Data School](http://www.dataschool.io/) - Data science tutorials for beginners! 246 | - [Colah's Blog](http://colah.github.io) - Blog for understanding Neural Networks! 247 | - [Sebastian's Blog](http://sebastianruder.com/#open) - Blog for NLP and transfer learning! 248 | - [Distill](http://distill.pub) - Dedicated to clear explanations of machine learning! 249 | 250 | ## Podcasts 251 | 252 | - [Adversarial Learning](http://adversariallearning.com/) 253 | - [Becoming a Data Scientist](https://www.becomingadatascientist.com/category/podcast/) 254 | - [Data Crunch](http://vaultanalytics.com/datacrunch/) 255 | - [Data Skeptic](https://dataskeptic.com/) 256 | - [Data Stories](http://datastori.es/) 257 | - [Learning Machines 101](http://www.learningmachines101.com/) 258 | - [Linear Digressions](http://lineardigressions.com/) 259 | - [Not So Standard Deviations](https://soundcloud.com/nssd-podcast) 260 | - [Partially Derivative](http://partiallyderivative.com/) 261 | - [Superdatascience](https://www.superdatascience.com/podcast/) 262 | - [What's The Point](https://fivethirtyeight.com/tag/whats-the-point/) 263 | 264 | ## Books 265 | - [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) 266 | - [The Data Science Handbook](http://www.thedatasciencehandbook.com/) 267 | - [The Art of Data Usability](https://www.manning.com/books/the-art-of-data-usability) - Early access 268 | - [Think Like a Data Scientist](https://www.manning.com/books/think-like-a-data-scientist) 269 | - [R in Action, Second Edition](https://www.manning.com/books/r-in-action-second-edition) 270 | - [Introducing Data Science](https://www.manning.com/books/introducing-data-science) 271 | - [Practical Data Science with R](https://www.manning.com/books/practical-data-science-with-r) 272 | - [Exploring Data Science](https://www.manning.com/books/exploring-data-science) - free eBook sampler 273 | - [Exploring the Data Jungle](https://www.manning.com/books/exploring-the-data-jungle) - free eBook sampler 274 | 275 | ## Facebook Accounts 276 | 277 | - [Data](https://www.facebook.com/data) 278 | - [Big Data Scientist](https://www.facebook.com/Bigdatascientist) 279 | - [Data Science 101](https://www.facebook.com/DataScience101) 280 | - [Data Science Day](https://www.facebook.com/DataScienceDay/) 281 | - [Data Science Academy](https://www.facebook.com/nycdatascience) 282 | - [Facebook Data Science Page](https://www.facebook.com/pages/Data-science/431299473579193?ref=br_rs) 283 | - [Data Science London](https://www.facebook.com/pages/Data-Science-London/226174337471513) 284 | - [Data Science Technology and Corporation](https://www.facebook.com/DataScienceTechnologyCorporation?ref=br_rs) 285 | - [Data Science - Closed Group](https://www.facebook.com/groups/1394010454157077/?ref=br_rs) 286 | - [Center for Data Science](https://www.facebook.com/centerdatasciences?ref=br_rs) 287 | - [Big data hadoop NOSQL Hive Hbase](https://www.facebook.com/groups/bigdatahadoop/) 288 | - [Analytics, Data Mining, Predictive Modeling, Artificial Intelligence](https://www.facebook.com/groups/data.analytics/) 289 | - [Big Data Analytics using R](https://www.facebook.com/groups/434352233255448/) 290 | - [Big Data Analytics with R and Hadoop](https://www.facebook.com/groups/rhadoop/) 291 | - [Big Data Learnings](https://www.facebook.com/groups/bigdatalearnings/) 292 | - [Big Data, Data Science, Data Mining & Statistics](https://www.facebook.com/groups/bigdatastatistics/) 293 | - [BigData/Hadoop Expert](https://www.facebook.com/groups/BigDataExpert/) 294 | - [Data Mining / Machine Learning / AI](https://www.facebook.com/groups/machinelearningforum/) 295 | - [Data Mining/Big Data - Social Network Ana](https://www.facebook.com/groups/dataminingsocialnetworks/) 296 | - [Vademecum of Practical Data Science](https://www.facebook.com/datasciencevademecum) 297 | - [Veri Bilimi Istanbul](https://www.facebook.com/groups/veribilimiistanbul/) 298 | - [The Data Science Blog](https://www.facebook.com/theDataScienceBlog/) 299 | 300 | ## Twitter Accounts 301 | 302 | - [Big Data Combine](https://twitter.com/BigDataCombine) - Rapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies 303 | - [Big Data Mania](https://twitter.com/BigDataGal) - Data Viz Wiz | Data Journalist | Growth Hacker | Author of Data Science for Dummies (2015) 304 | - [Big Data Science](https://twitter.com/analyticbridge) - Big Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research. 305 | - [Charlie Greenbacker](https://twitter.com/greenbacker) - Director of Data Science at @ExploreAltamira 306 | - [Chris Said](https://twitter.com/Chris_Said) - Data scientist at Twitter 307 | - [Clare Corthell](https://twitter.com/clarecorthell) - Dev, Design, Data Science @mattermark #hackerei 308 | - [DADI Charles-Abner](https://twitter.com/DadiCharles) - #datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast 309 | - [Data Science Central](https://twitter.com/DataScienceCtrl) - Data Science Central is the industry's single resource for Big Data practitioners. 310 | - [Data Science London](https://twitter.com/ds_ldn) Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data 311 | - [Data Science Renee](https://twitter.com/BecomingDataSci) - Documenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist 312 | - [Data Science Report](https://twitter.com/TedOBrien93) - Mission is to help guide & advance careers in Data Science & Analytics 313 | - [Data Science Tips](https://twitter.com/datasciencetips) - Tips and Tricks for Data Scientists around the world! #datascience #bigdata 314 | - [Data Vizzard](https://twitter.com/DataVisualizati) - DataViz, Security, Military 315 | - [DataScienceX](https://twitter.com/DataScienceX) 316 | - [deeplearning4j](https://twitter.com/deeplearning4j) - 317 | - [DJ Patil](https://twitter.com/dpatil) - White House Data Chief, VP @ RelateIQ. 318 | - [Domino Data Lab](https://twitter.com/DominoDataLab) 319 | - [Drew Conway](https://twitter.com/drewconway) - Data nerd, hacker, student of conflict. 320 | - [Emilio Ferrara](https://twitter.com/jabawack) - #Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv 321 | - [Erin Bartolo](https://twitter.com/erinbartolo) - Running with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr. 322 | - [Greg Reda](https://twitter.com/gjreda) Working @ _GrubHub_ about data and pandas 323 | - [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. 324 | - [Hakan Kardas](https://twitter.com/hakan_kardes) - Data Scientist 325 | - [Hilary Mason](https://twitter.com/hmason) - Data Scientist in Residence at @accel. 326 | - [Jeff Hammerbacher](https://twitter.com/hackingdata) ReTweeting about data science 327 | - [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. 328 | - [Juan Miguel Lavista](https://twitter.com/BDataScientist) - Principal Data Scientist @ Microsoft Data Science Team 329 | - [Julia Evans](https://twitter.com/b0rk) - Hacker - Pandas - Data Analyze 330 | - [Kenneth Cukier](https://twitter.com/kncukier) - The Economist's Data Editor and co-author of Big Data (http://big-data-book.com ). 331 | - [Kevin Davenport](https://twitter.com/KevinLDavenport) - Organizer of https://meetup.com/San-Diego-R-Users-Group/ 332 | - [Kevin Markham](https://twitter.com/justmarkham) - Data science instructor, and founder of [Data School](http://www.dataschool.io/) 333 | - [Kim Rees](https://twitter.com/krees) - Interactive data visualization and tools. Data flaneur. 334 | - [Kirk Borne](https://twitter.com/KirkDBorne) - DataScientist, PhD Astrophysicist, Top #BigData Influencer. 335 | - [Linda Regber](https://twitter.com/LindaRegber) - Data story teller, visualizations. 336 | - [Luis Rei](https://twitter.com/lmrei) - PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science. 337 | - [Mark Stevenson](https://twitter.com/Agent_Analytics) - Data Analytics Recruitment Specialist at Salt (@SaltJobs) | Analytics - Insight - Big Data - Datascience 338 | - [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. 339 | - [Matthew Russell](https://twitter.com/ptwobrussell) - Mining the Social Web. 340 | - [Mert Nuhoğlu](https://twitter.com/mertnuhoglu) Data Scientist at BizQualify, Developer 341 | - [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. 342 | - [Noah Iliinsky](https://twitter.com/noahi) - Visualization & interaction designer. Practical cyclist. Author of vis books: http://www.oreilly.com/pub/au/4419 343 | - [Paul Miller](https://twitter.com/PaulMiller) - Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst. 344 | - [Peter Skomoroch](https://twitter.com/peteskomoroch) - Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks 345 | - [Prash Chan](https://twitter.com/MDMGeek) - Solution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud. 346 | - [Quora Data Science](https://twitter.com/q_datascience) Quora's data science topic 347 | - [R-Bloggers](https://twitter.com/Rbloggers) - Tweet blog posts from the R blogosphere, data science conferences and (!) open jobs for data scientists. 348 | - [Rand Hindi](https://twitter.com/randhindi) 349 | - [Randy Olson](https://twitter.com/randal_olson) - Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate. 350 | - [Recep Erol](https://twitter.com/EROLRecep) - Data Science geek @ UALR 351 | - [Ryan Orban](https://twitter.com/ryanorban) - Data scientist, genetic origamist, hardware aficionado 352 | - [Sean J. Taylor](https://twitter.com/seanjtaylor) - Social Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics. 353 | - [Silvia K. Spiva](https://twitter.com/silviakspiva) - #DataScience at Cisco 354 | - [Spencer Nelson](https://twitter.com/spenczar_n) - Data nerd 355 | - [Talha Oz](https://twitter.com/tozCSS) - Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile kaggler/data scientist 356 | - [Tasos Skarlatidis](https://twitter.com/anskarl) - Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source. 357 | - [Terry Timko](https://twitter.com/Terry_Timko) - InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence 358 | - [Tony Baer](https://twitter.com/TonyBaer) - IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in. 359 | - [Tony Ojeda](https://twitter.com/tonyojeda3) - Data Scientist | Author | Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC 360 | - [Vamshi Ambati](https://twitter.com/vambati) - Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com ) 361 | - [Wes McKinney](https://twitter.com/wesmckinn) - Pandas (Python Data Analysis library). 362 | - [WileyEd](https://twitter.com/WileyEd) - Senior Manager - @Seagate Big Data Analytics | @McKinsey Alum | #BigData + #Analytics Evangelist | #Hadoop, #Cloud, #Digital, & #R Enthusiast 363 | - [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. 364 | @SkymindIO's open-source deep learning for the JVM. Integrates with Hadoop, Spark. Distributed GPU/CPUs | http://nd4j.org | https://www.skymind.ai/ 365 | 366 | ## Youtube Videos & Channels 367 | 368 | - [What is machine learning?](https://www.youtube.com/watch?v=WXHM_i-fgGo) 369 | - [Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) 370 | - [Deep Learning: Intelligence from Big Data](https://www.youtube.com/watch?v=czLI3oLDe8M) 371 | - [Interview with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton](https://www.youtube.com/watch?v=1Wp3IIpssEc) 372 | - [Introduction to Deep Learning with Python](https://www.youtube.com/watch?v=S75EdAcXHKk) 373 | - [What is machine learning, and how does it work?](https://www.youtube.com/watch?v=elojMnjn4kk) 374 | - [Data School](https://www.youtube.com/channel/UCnVzApLJE2ljPZSeQylSEyg) - Data Science Education 375 | - [Neural Nets for Newbies by Melanie Warrick (May 2015)](https://www.youtube.com/watch?v=Cu6A96TUy_o) 376 | - [Neural Networks video series by Hugo Larochelle](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) 377 | - [Google DeepMind co-founder Shane Legg - Machine Super Intelligence](https://www.youtube.com/watch?v=evNCyRL3DOU) 378 | - [Data Science Primer](https://www.youtube.com/watch?v=cHzvYxBN9Ls&list=PLPqVjP3T4RIRsjaW07zoGzH-Z4dBACpxY) 379 | 380 | ## Toolboxes - Environment 381 | 382 | * [Datalab from Google](https://cloud.google.com/datalab/docs/) easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively. 383 | * [Hortonworks Sandbox](http://hortonworks.com/products/sandbox/) is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials. 384 | * [R](http://www.r-project.org/) is a free software environment for statistical computing and graphics. 385 | * [RStudio](https://www.rstudio.com) IDE – powerful user interface for R. It’s free and open source, works onWindows, Mac, and Linux. 386 | * [Python - Pandas - Anaconda](https://www.continuum.io/downloads) Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing 387 | * [Scikit-Learn](http://scikit-learn.org/stable/) Machine Learning in Python 388 | * [NumPy](http://www.numpy.org/) NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays. 389 | * [SciPy](https://www.scipy.org/) SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization. 390 | * [Data Science Toolbox](https://www.coursera.org/learn/data-scientists-tools) - Coursera Course 391 | * [Data Science Toolbox](http://datasciencetoolbox.org/) - Blog 392 | * [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. 393 | * [Sense Data Science Development Platform](https://sense.io/) A New Cloud Platform for Data Science and Big Data Analytics 394 | 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. 395 | * [Datadog](https://www.datadoghq.com/) Solutions, code, and devops for high-scale data science. 396 | * [Variance](http://variancecharts.com/) Build powerful data visualizations for the web without writing JavaScript 397 | * [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. 398 | * [Domino Data Labs](http://www.dominodatalab.com) Run, scale, share, and deploy your models — without any infrastructure or setup. 399 | * [Apache Flink](http://flink.apache.org/) A platform for efficient, distributed, general-purpose data processing. 400 | * [Apache Hama](http://hama.apache.org/) Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. 401 | * [Weka](http://www.cs.waikato.ac.nz/ml/weka/) Weka is a collection of machine learning algorithms for data mining tasks. 402 | * [Octave](https://www.gnu.org/software/octave/) GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab) 403 | * [Apache Spark](https://spark.apache.org/) Lightning-fast cluster computing 404 | * [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) - a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services. 405 | * [Caffe](http://caffe.berkeleyvision.org/) Deep Learning Framework 406 | * [Torch](http://torch.ch/) A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT 407 | * [Nervana's python based Deep Learning Framework](https://github.com/NervanaSystems/neon) 408 | * [Skale](https://github.com/skale-me/skale-engine) - High performance distributed data processing in NodeJS 409 | * [Aerosolve](http://airbnb.io/aerosolve/) - A machine learning package built for humans. 410 | * [Intel framework](https://github.com/01org/idlf) - Intel® Deep Learning Framework 411 | * [Datawrapper](https://www.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) 412 | * [Tensor Flow](https://www.tensorflow.org/) - TensorFlow is an Open Source Software Library for Machine Intelligence 413 | * [Natural Language Toolkit](http://www.nltk.org/) 414 | * [nlp-toolkit for node.js](https://www.npmjs.com/package/nlp-toolkit) 415 | * [Julia](http://julialang.org) – high-level, high-performance dynamic programming language for technical computing 416 | * [IJulia](https://github.com/JuliaLang/IJulia.jl) – a Julia-language backend combined with the Jupyter interactive environment 417 | * [Apache Zeppelin](http://zeppelin.apache.org/) - Web-based notebook that enables data-driven, 418 | interactive data analytics and collaborative documents with SQL, Scala and more 419 | * [Featuretools](https://github.com/featuretools/featuretools/) - An open source framework for automated feature engineering written in python 420 | * [Optimus](https://github.com/ironmussa/Optimus) - Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend. 421 | * [Albumentations](https://github.com/albu/albumentations) - А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops. 422 | 423 | Quick Start 424 | 425 | 426 | ## Visualization Tools - Environments 427 | 428 | * [addepar](http://opensource.addepar.com/ember-charts/#/overview) 429 | * [amcharts](https://www.amcharts.com/) 430 | * [anychart](http://www.anychart.com/) 431 | * [slemma](https://slemma.com/) 432 | * [cartodb](http://cartodb.github.io/odyssey.js/) 433 | * [Cube](http://square.github.io/cube/) 434 | * [d3plus](http://d3plus.org/) 435 | * [Data-Driven Documents(D3js)](https://d3js.org/) 436 | * [datahero](https://datahero.com/) 437 | * [dygraphs](http://dygraphs.com/) 438 | * [ECharts](http://echarts.baidu.com/index-en.html) 439 | * [exhibit](http://www.simile-widgets.org/exhibit/) 440 | * [Gatherplot](http://www.gatherplot.org/) 441 | * [gephi](https://gephi.org/) 442 | * [ggplot2](http://ggplot2.org/) 443 | * [Glue](http://www.glueviz.org/en/latest/) 444 | * [Google Chart Gallery](https://developers.google.com/chart/interactive/docs/gallery) 445 | * [highcarts](http://www.highcharts.com/) 446 | * [import.io](https://www.import.io/) 447 | * [jqplot](http://www.jqplot.com/) 448 | * [Matplotlib](http://matplotlib.org/) 449 | * [nvd3](http://nvd3.org/) 450 | * [Opendata-tools](http://opendata-tools.org/en/visualization/) - list of open source data visualization tools 451 | * [Openrefine](http://openrefine.org/) 452 | * [plot.ly](https://plot.ly/) 453 | * [raw](http://rawgraphs.io) 454 | * [rcharts](http://rcharts.io/) 455 | * [techanjs](http://techanjs.org/) 456 | * [tenxer](http://tenxer.github.io/xcharts/) 457 | * [Timeline](http://timeline.knightlab.com/) 458 | * [variancecharts](http://variancecharts.com/index.html) 459 | * [vida](https://vida.io/) 460 | * [Wolframalpha](http://www.wolframalpha.com/) 461 | * [Wrangler](http://vis.stanford.edu/wrangler/) 462 | * [r2d3](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) 463 | * [NetworkX](https://networkx.github.io/) - High-productivity software for complex networks 464 | 465 | 466 | ## Journals, Publications and Magazines 467 | 468 | * [ICML](http://icml.cc/2015/) - International Conference on Machine Learning 469 | * [epjdatascience](http://epjdatascience.springeropen.com/) 470 | * [Journal of Data Science](http://www.jds-online.com/) - an international journal devoted to applications of statistical methods at large 471 | * [Big Data Research](https://www.journals.elsevier.com/big-data-research) 472 | * [Journal of Big Data](http://journalofbigdata.springeropen.com/) 473 | * [Big Data & Society](http://journals.sagepub.com/home/bds) 474 | * [Data Science Journal](https://www.jstage.jst.go.jp/browse/dsj) 475 | * [datatau.com/news](http://www.datatau.com/news) - Like Hacker News, but for data 476 | * [Data Science Trello Board](https://trello.com/b/rbpEfMld/data-science) 477 | * [Medium Data Science Topic](https://medium.com/topic/data-science) - Data Science related publications on medium 478 | 479 | 480 | ## Presentations 481 | 482 | * [How to Become a Data Scientist](http://www.slideshare.net/ryanorban/how-to-become-a-data-scientist) 483 | * [Introduction to Data Science](http://www.slideshare.net/NikoVuokko/introduction-to-data-science-25391618) 484 | * [Intro to Data Science for Enterprise Big Data](http://www.slideshare.net/pacoid/intro-to-data-science-for-enterprise-big-data) 485 | * [How to Interview a Data Scientist](http://www.slideshare.net/dtunkelang/how-to-interview-a-data-scientist) 486 | * [How to Share Data with a Statistician](https://github.com/jtleek/datasharing) 487 | * [The Science of a Great Career in Data Science](http://www.slideshare.net/katemats/the-science-of-a-great-career-in-data-science) 488 | * [What Does a Data Scientist Do?](http://www.slideshare.net/datasciencelondon/big-data-sorry-data-science-what-does-a-data-scientist-do) 489 | * [Building Data Start-Ups: Fast, Big, and Focused](http://www.slideshare.net/medriscoll/driscoll-strata-buildingdatastartups25may2011clean) 490 | * [How to win data science competitions with Deep Learning](http://www.slideshare.net/0xdata/how-to-win-data-science-competitions-with-deep-learning) 491 | 492 | ## Competitions 493 | 494 | Some data mining competition platforms 495 | * [Kaggle](https://www.kaggle.com/) 496 | * [DrivenData](https://www.drivendata.org/) 497 | * [Analytics Vidhya](https://www.analyticsvidhya.com/blog/tag/data-science-competitions/) 498 | * [The Data Science Game](http://www.datasciencegame.com/) 499 | * [InnoCentive](https://www.innocentive.com/) 500 | * [TuneedIT](http://tunedit.org/challenges) 501 | 502 | ## Comics 503 | ![Digital Data](http://imgs.xkcd.com/comics/digital_data.png "Digital Data") 504 | 505 | ## Tutorials 506 | 507 | * [Data science your way](https://github.com/jadianes/data-science-your-way) 508 | 509 | ## Other Awesome Lists 510 | 511 | - Other amazingly awesome lists can be found in the [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness) list. 512 | - [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) A curated list of awesome Machine Learning frameworks, libraries and software. 513 | - [lists](https://github.com/jnv/lists) 514 | - [awesome-dataviz](https://github.com/fasouto/awesome-dataviz) 515 | - [awesome-python](https://github.com/vinta/awesome-python) 516 | - [Data Science IPython Notebooks.](https://github.com/donnemartin/data-science-ipython-notebooks) 517 | - [awesome-r](https://github.com/qinwf/awesome-R) 518 | - [awesome-datasets](https://github.com/caesar0301/awesome-public-datasets) – An awesome list of high-quality open datasets in public domains 519 | - [awesome-Machine Learning & Deep Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md) 520 | - [Awesome Data Science Ideas](https://github.com/JosPolfliet/awesome-datascience-ideas) 521 | - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) 522 | - [Community Curated Data Science Resources](https://hackr.io/tutorials/learn-data-science) 523 | - [Awesome Machine Learning On Source Code](https://github.com/src-d/awesome-machine-learning-on-source-code) 524 | --------------------------------------------------------------------------------