The Full List In One Page
Of Data Science Resource

Resource Type Resource List
Data Science Books
A list of books covering Data Analysis, Data Science, Machine Learning, Data Visualization, Statistics & Associated Programming Languages

Book of the Month


Data Scientists at Work
A collection of interviews with 16 of the world's most influential and innovative data scientists from across the spectrum of this hot new profession - from Yann LeCun at Facebook, to Daniel Tunkelang at LinkedIn, to Caitlin Smallwood at Netflix, to Jake Porway at DataKind and more ...

General


Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving


Data Driven: Profiting from Your Most Important Business Asset


Competing on Analytics: The New Science of Winning


Data Analysis with Open Source Tools


Data Source Handbook


Who's #1?: The Science of Rating and Ranking


Data Science


Doing Data Science: Straight Talk from the Frontline


Data Smart: Using Data Science to Transform Information into Insight


Data Science for Business:
What you need to know about data mining and data-analytic thinking



Statistics


An Introduction to Statistical Learning: with Applications in R


Data Analysis Using Regression and Multilevel/Hierarchical Models


Statistics As Principled Argument


A Handbook of Statistical Analyses Using R, Second Edition


Mathematical Statistics and Data Analysis (with CD Data Sets)


Machine Learning


Pattern Recognition and Machine Learning


Bayesian Reasoning and Machine Learning


Machine Learning: A Probabilistic Perspective


The LION Way: Learning plus Intelligent Optimization


Natural Language Processing (NLP)


Speech and Language Processing, 2nd Edition


Foundations of Statistical Natural Language Processing


Natural Language Processing with Python


Graph-based Natural Language Processing and Information Retrieval


Natural Language Processing for Online Applications: Text retrieval, extraction and categorization. Second revised edition


Data Visualization


Visualizing Data: Exploring & Explaining Data with Processing Environment


The Visual Display of Quantitative Information


Data Mining


Mining of Massive Datasets


Data-Intensive Text Processing with MapReduce


Data Mining with R: Learning with Case Studies


R


The Art of R Programming: A Tour of Statistical Software Design


R Graphics Cookbook


Python


Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython


Learning Python, 5th Edition


Spark


Learning Spark: Lightning-fast big data analytics


Fast Data Processing with Spark


Big Data Tools


Hadoop: The Definitive Guide


Programming Pig


Practical Cassandra: A Developer's Approach


Data Science Meetups
A list of Data Science Meetups from around the world

New York City

San Francisco

Los Angeles

Washington DC

Boston

Chicago

Seattle

Denver

Austin

Atlanta

Toronto

Montreal

Vancouver

London

Berlin

Paris

Amsterdam

Tel Aviv

Dubai

Delhi

Bangalore

Singapore

Sydney

Data Science MOOCs
A list of Data Science related MOOCs

Machine Learning


AI


Data Analysis


Programming

Data Science Datasets
A list of publicly available datasets

General

  • Amazon Public Data Sets
    Public Data Sets on AWS: centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications
  • Wikipedia
    Wikipedia offers free copies of all available content to interested users. These databases can be used for mirroring, personal use, informal backups, offline use or database queries
  • Freebase
    A community-curated database of people, places and things
  • World Bank
    DataBank is an analysis and visualization tool that contains collections of time series data on a variety of topics
  • Windows Azure Marketplace
    Free datasets via Windows Azure Data Market including Academic data, Speech Recognition data, etc.
  • Machine Learning Repository
    200+ Datasets from Center for ML & Intelligent Systems
  • Deep Learning Data Sets
    Music, natural images, text, speech, faces, recommendation systems datasets for benchmarking algorithms
  • Stanford Large Network Dataset Collection
    A collection of about 50 large network datasets from tens of thousands of nodes and edges to tens of millions of nodes and edges. It includes social networks, web graphs, road networks, internet networks, citation networks, collaboration networks, and communication networks.
  • Yahoo Datasets
    We have various types of data available to share. They are categorized into Ratings, Language, Graph, Advertising and Market Data, Computing Systems and an appendix of other relevant data and resources available via the Yahoo! Developer Network.

    And, if you are looking for something specific, you can always try your luck posting on reddit/r/datasets or on Open Data StackExchange


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Data Science Most Read Articles
Most read articles from the Data Science Weekly Newsletter by Quarter

Q2 2014

  • Why becoming a Data Scientist is NOT actually easier than you think
    I was just doing some late night reading and came across this article. TL;DR - You can take the ML course on Coursera and you're magically a Data Scientist, because three really intelligent people did it. I disagree...

  • Data scientists need their own GitHub. Here are four of the best options
    Imagine if a company’s three highly valued data scientists can happily work together without duplicating each other’s efforts and can easily call up the ingredients and results of each other’s previous work. That day has come...

  • Getting started in Data Science: My thoughts [Trey Causey]
    There's no denying that 'data scientist' is a hot job title to have right now, and for good reason. It's a tremendously fun and challenging field to be in, and despite all of the often undeserved hoopla that surrounds it, data scientists are doing some pretty amazing things. So it's no surprise that many people are clamoring to find out how to become data scientists. As I run a blog that attempts to teach some basic data science using sports analytics, I often get email asking how one gets started in data science and/or how quickly one can learn the prerequisites for being a data scientist. Instead of replying to these all the time, I thought I'd write my thoughts up here...

  • Spark is on fire
    Spark is on the rise, to an even greater degree than I thought last month...

  • Data Workflows for Machine Learning
    In this in-depth video, we compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. ...

  • Elusive Data Scientists Driving High Salaries
    Data scientists, the elusive kingpins in the Big Data movement, are earning base salaries of well over $200K, are younger, overwhelmingly male, have at least a master’s degree and probably a Ph.D., and one in three are foreign born, according to the first-ever study looking at salaries, education levels, gender and geographical location of this new profession...

  • Deep Learning - How & Why Deep Learning Methods Work
    The recent resurrection of multi-layer neural networks is generating a lot of interest currently, with deep learning appearing on the New York Times front page, and big companies like Google and Facebook hunting for the experts in this field. Jürgen’s talk sheds more light on how deep learning methods work, and why they work...

  • Why The R Programming Language Is Good For Business
    Thanks to one company, the same code that is revolutionizing the scientific community is now moving up the ranks of the business world...

  • What is the Difference Between Artificial Intelligence, Machine Learning, Statistics, and Data Mining
    I assume the author of that question is trying to get a clear picture by understanding the line of separation that distinguish each field from the other. So here is my take to explain it in a more simplified way that I ever could do...

  • META: What Data Scientists are reading. And why.
    We recently posted an analysis of the most-read articles on this newsletter for the past two quarters. We were curious to understand what was getting the most clicks and if there were any consistent areas of interest...



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Q1 2014

  • Machine Learning in 10 pictures
    I find myself coming back to the same few pictures when explaining basic machine learning concepts. Below is a list I find most illuminating...

  • The $30/hr Data Scientist
    Yesterday a journalist asked me to comment on Vincent Granville's post about the $30/hr data scientist for hire on Elance. What started as a quick reply in an email, spiraled a bit, so I figured I'd post the entire reply here to get your thoughts in the comments...

  • "How do I become a Data Scientist?"
    I got an email recently asking something along these lines: "I'm a smart ex-engineer who likes stats. I want to be a data scientist. How difficult will it be for me to find a job doing data science work at a startup?". I sent back an email which looked more or less like the following post...

  • 10 surprising Machine Learning applications
    You may have heard that today's tech companies are using machine learning to identify and filter email spam (Google), blacklist and penalize spam blogs so that users get good search results (also Google), recommend products specifically for you (Amazon), and fight fraud (IBM). Today's post isn't about that. It's about the new, perhaps surprising ways that companies (and non-profits) are using machine learning to make smarter, faster, better products...

  • How I made $500k with Machine Learning and High Frequency Trading
    This post will detail what I did to make approx. 500k from high frequency trading from 2009 to 2010. Since I was trading completely independently and am no longer running my program I’m happy to tell all. My trading was mostly in Russel 2000 and DAX futures contracts...

  • A non-comprehensive list of awesome things other people did this year
    I made this list off the top of my head and have surely missed awesome things people have done this year... I wrote this post because a blog often feels like a place to complain, but we started Simply Stats as a place to be pumped up about the stuff people were doing with data...

  • How to Speed up a Python Program 114,000 times
    Optimizations are one thing -- making a serious data collection program run 114,000 times faster is another thing entirely. Leaning on 30+ years of programming experience, David Schachter goes over all the optimizations he made to his (secret) company's data-collecting program to get such massive performance gains. In doing so, he might be able to teach you a thing or two about optimizing a python program...

  • Flappy Bird hack using Reinforcement Learning
    This is a hack for the popular game, Flappy Bird. After playing the game a few times, I saw the opportunity to practice my machine learning skills and try and get Flappy Bird to learn how to play the game by itself...

  • Is Julia the Future for Big Data Analytics?
    In many Big Data blogs, meetups and in the halls of the most recent O’Reilly Strata Conference, one of the most-discussed topics is which language is better for data analysis: Python or R. Some of the talk has even reached “religious” overtones not unlike previous discussions on Windows vs. Linux or Microsoft’s Internet Explorer vs. Mozilla Firefox. So what’s the issue here?...

  • Difference between Data Scientist and Data Analyst
    Jobs related to Data Science have topped the charts in job portals. There are job openings for various job titles like Data Scientists, Data Analysts, and Data Engineers. Though all these job titles deal with data and sound similar, they do have a number of detailed differences. Ever wondered how different they are from each other? I did! And here are the differences I found between a Data Scientist and a Data Analyst...


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Q4 2013

  • How Python became the language of choice for Data Science
    Nowadays Python is probably the programming language of choice (besides R) for data scientists for prototyping, visualization, and running data analyses on small and medium sized data sets. And rightly so, I think, given the large number of available tools. However, it wasn’t always like this...

  • Stanford algorithm analyzes sentence sentiment, advances Machine Learning
    The program, dubbed NaSent – short for Neural Analysis of Sentiment – is a new development in a field of computer science known as “Deep Learning” that aims to give computers the ability to acquire new understandings in a more human-like way...

  • How to find the bars that women love
    Jetpac City Guides tells you all about the best places in every city to hit, based on analyzing millions of Instagram photos. It uses some pretty cool big data technology to look at the photos, understand what's going on in them (are people smiling? what are they wearing?) and match them to their GPS locations...

  • Scryer: Netflix's Predictive Auto Scaling Engine - Part 2
    In Part 1 of this series, we introduced Scryer, Netflix’s predictive autoscaling engine, and discussed its use cases and how it runs in Netflix. In this second installment, we will discuss the design of Scryer ranging from the technical implementation to the algorithms that drive its predictions...

  • You might be a Data Scientist if
    As I meet up-and-coming data scientists, I've realized that we share a surprising number of very specific experiences. Here's a list of things of these data science rites of passage, in no particular order...

  • This Data Scientist spent a year deep inside The New York Times. Here’s what he discovered
    Brian Abelson spent the last year at The Times using data and analytics to understand Times content. Abelson had access to one of the most coveted datasets in publishing, The New York Times’ web and social traffic...we talk to Abelson about his year at The Times, as he attempted to create a better set of metrics focused on measurements of human response to media, like impact and behavioral change...

  • Uber's Data Scientist on the importance of knowing one thing about everybody
    Data scientist Bradley Voytek recently spoke about his work at car service Uber. He explained how user information with location and temporal data could be analyzed to find unexpected and useful correlations...

  • What I learnt from 2 years of 'Data Sciencing'
    Last week was my last day at uSwitch.com. From becoming aware of data scientist as a valid job title on my job offer letter, to speaking at Strata London, to signing a book deal to write about it in our book on Web Data Mining (that's progressing at a glacial pace), I figured that I should jot down some takeaway lessons while this experience is still fresh...

  • New to Data Science
    Get started on the path toward becoming a data science practitioner with this helpful list of resources...

  • Online Learning Curriculum for Data Scientists
    “Is there any online reading or courses I can do to get into data analysis?”... I get asked this question a lot in the workplace. In this post I propose a learning path to “get into data analysis”...



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Data Scientist Talks
A list of talks from prominent Data Scientists

Drew Conway


Geoff Hinton


Yann LeCun


Hilary Mason


Josh Wills

Data Scientists on Twitter
A list of influential Data Scientists on Twitter


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Data Science Blogs
A list of some of the most popular blogs related to Data Science.

Mainstream

  • FiveThirtyEight:
    Nate Silver (and other contributor)'s blog

  • The Numbers Guy:
    Carl Bialik writing in the Wall Street Journal examines the way numbers are used, and abused.

  • Freakonomics:
    The authors of Freakonomics and SuperFreakonomics (and friends) "continue to spout off".

  • OKCupid:
    Compilation of observations and statistics from millions of OkCupid user interactions - to explore the data side of the online world.

  • Data Ranker:
    Opinion data collected on Ranker.com. Similar to okcupid's blog, but with different types of data.

  • Beyond The Purchase:
    About data applied to consumer psychology.

  • Data Science & Psychology:
    Data Science applied to Values, Morals, Politics, & things that matter


Machine Learning


Statistics



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