Due to the broad definition of Data Science, **the number of resources on the field is huge** (and keeps growing). In this section you will find a **selection of the best data science resources classified by type of resource**. The goal is to keep an archive of everything we found useful and interesting.

If this is your **first time** in *All Data Science*, or if you prefer to have a **guide detailed by topic**, we recommend you to first check our start page.

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### Resources index:

#### 1. Data Science links

2. Data Science books

3. Data Science videos

4. Data Science courses

5. Data Science websites

6. Data Science podcasts

7. Data Science software

8. Data Science datasets

9. Data Science influencers

10. Data Science formal education

## 1. Data Science links

#### Data Science motivation links:

**Data Scientist: The Sexiest Job of the 21st Century – Thomas H. Davenport and D.J. Patil (Harvard Business Review)****Data Science: The Numbers of Our Lives – Claire Cain Miller (The New York Times)****Why Data Science Is the Fastest Growing Industry in Tech Right Now – Import.io****The Most Promising Jobs Of 2016 – Kathryn Dill (Forbes)****The Supply And Demand Of Data Scientists: What The Surveys Say – Gil Press (Forbes)****Data Science: Future Or Fiction? – John Edwards (Forbes)****How is Big Data Changing the World? – Data Science Central.****9 Ways Big Data and Analytics are Changing the World (For the Better) – Targit.****How is big data going to change the world? – The World Economic Forum.****Order From Chaos: How Big Data Will Change the World – Visual Capitalist.**

#### Data Science description links:

**Data Science – Wikipedia****Data Science: An Introduction – Wikibooks****What is Data Science? – Frank Lo (DataJobs)****What Is a Data Scientist? – Quora****What Is Data Science? – Yanir Seroussi****Getting Started in Data Science – Trey Causey****How Do I Become a Data Scientist? – Spencer Nelson****A Taxonomy of Data Science – Hilary Mason and Chris Wiggins (Dataists)****The Data Science Venn Diagram – Drew Conway****Beyond the Venn diagram – Daniel Tunkelang (O’Reilly)****What Questions Can Data Science Answer? – Brandon Rohrer (KDnuggets)****5 Groups of Data Scientists: Which Group Are You In? – Manu Jeevan (Big Data Made Simple)****The Data Behind Today’s Data Scientists: An Infographic – Justin Tenuto (Crowdflower)**

#### Data Science history links:

**A Very Short History Of Data Science – Gil Press (Forbes)****The First Ever Data Scientist – Ryan Swanstrom (Data Science 101)****Statistics = Data Science? – C. F. Jeff Wu****50 Years of Data Science – David Donoho**

#### Data Science mistakes links:

**Disconnect between CIOs and LOB managers weakens data quality – CIO.****Enterprises Don’t Have Big Data, They Just Have Bad Data – TechCrunch.****Bad Data, Poor Quality? The Impact of Ineffective Information – Datafloq.****Correlation, Causation, and Confusion – The New Atlantis.****Causation vs Correlation – Stats.org.****Examples for teaching: Correlation does not mean causation – Cross Validated.****Spurious Correlations – Tyler Vigen.****Avoiding Complexity of Machine Learning Systems – Quora.****What is an intuitive explanation of overfitting? – Quora.****Overfitting – Investopedia.****The Danger of Overfitting Regression Models – The Minitab Blog.****7 Common Data Science Mistakes and How to Avoid Them – KDnuggets.****6 Worst Mistakes for Data Scientists, and How to Avoid Them – Ucanalytics.****What are the most common mistakes made by aspiring data scientists? – Quora****The Mistakes Companies Make With Big Data – Wall Street Journal.****Managing big data: the two biggest mistakes companies make – Search Business Analytics.****How to Avoid Big Mistakes in Big Data – Computer Sciences Corporation.**

#### Data Science several links:

**Do I Need a Masters/PhD to Become a Data Scientist? – Quora****5 Things You Should Know Before Getting a Degree in Data Science – Daniel Levine (RJMetrics)****Data Won the U.S. Election. Now Can It Save the World? – Ted Greenwald (MIT Technology Review)****The Lessons of Moneyball for Big Data Analysis – Rich Miller (Data Center Knowledge)****Analyzing Minard’s Visualization Of Napoleon’s 1812 March – Joanne Cheng (Thoughtbot)****Ten Practical Big Data Benefits – Data Science Series****Netflix Recommendations: Beyond the 5 stars (Part 1) – The Netflix Tech Blog.****Netflix Recommendations: Beyond the 5 stars (Part 2) – The Netflix Tech Blog.****Detecting influenza epidemics using search engine query data – Nature.****What We Can Learn From the Epic Failure of Google Flu Trends – Wired.****Learning to Communicate: Marketing, IT, and Data Scientists – ClickZ.****How Data Scientists Can Improve Communications Skills – Dataversity.****Becoming a Data Scientist: Curriculum via Metromap – Swami Chandrasekaran.****The hardest parts of data science – Yanir Seroussi.****Why becoming a data scientist might be easier than you think – Gigaom.****Why becoming a data scientist is NOT actually easier than you think – Joseph Misiti.****You’re Not a Data Scientist – Chuck Russell.****Data science done well looks easy – and that is a big problem for data scientists – Simply Statistics.**

## 2. Data Science books

#### Data Science introduction books:

**What Is Data Science? – Mike Loukides (O’Reilly)****Data Driven: Creating a Data Culture – DJ Patil & Hilary Mason (O’Reilly)****Analyzing the Analyzers – Harlan Harris (O’Reilly)****The Evolution of Data Products – Mike Loukides (O’Reilly)****Data Smart – John W. Foreman****Data Science for Business – Foster Provost****Data Science for Dummies – Lillian Pierson****Competing on Analytics – Thomas H. Davenport and Jeanne G. Harris****Keeping Up with the Quants – Thomas H. Davenport and Jinho Kim****Going Pro in Data Science – Jerry Overton (O’Reilly)****The Field Guide to Data Science – Boozallen****The Art of Data Science – Roger D. Peng and Elizabeth Matsui****Data Science and Analytics for Ordinary People – Jeffrey Strickland**

#### Statistics introduction books:

**Naked Statistics – Charles Wheelan****OpenIntro Statistics – David M. Diez, Christopher D. Barr and Mine Çetinkaya-Rundel**

#### Machine Learning introduction books:

**The Future of Machine Intelligence – David Beyer (O’Reilly)**

## 3. Data Science videos

#### Data Science introduction videos:

**What Is a Data Scientist? – Mike Gualtieri****What Is a Data Scientist? (Intro to Data Science) – Udacity****Why Data Science Will Power the Future – Udacity****Data Science: Where Are We Going? – DJ Patil (O’Reilly)****Getting Started with Data – Hilary Mason****Dirty Secrets of Data Science – Hilary Mason****A Day in the Life of a Data Scientist – Gigs (RCR Wireless News)****The Life of a Data Scientist – Josh Wills (Airbnb)****How I Became a Data Scientist – Owen Zhang (Open Data Science)****The Perfect Data Science Experience 1 – Brian Caffo (Johns Hopkins University)****The Perfect Data Science Experience 2 – Brian Caffo (Johns Hopkins University)**

## 4. Data Science courses

#### Data Science introduction courses:

**A Crash Course in Data Science – Johns Hopkins University (Coursera)****Building a Data Science Team – Johns Hopkins University (Coursera)****Managing Data Analysis – Johns Hopkins University (Coursera)****Data Science in Real Life – Johns Hopkins University (Coursera)****The Data Scientist’s Toolbox – Johns Hopkins University (Coursera)****Business Metrics for Data-Driven Companies – Duke University (Coursera)****Intro to Data Analysis – Udacity****Intro to Data Science – Udacity**

## 5. Data Science websites

**Data Science topic – Quora****Data Analysis topic – Quora****Data Science site – Stack Exchange****Cross Validated site – Stack Exchange****Data Science subreddit – Reddit****Machine Learning subreddit – Reddit****DataTau – DataTau****Kaggle – Kaggle**

## 6. Data Science podcasts

**Data Skeptic – Data Skeptic****Partially Derivative – Partially Derivative****Data Stories – Data Stories****O’Reilly Data Show Podcast – O’Reilly****Talking Machines – Talking Machines****Linear Digressions – Udacity****Learning Machines 101 – Learning Machines 101**

## 7. Data Science software

#### Programming/Coding software:

**Python – Python Software Foundation****R Project – R Foundation****NumPy – The SciPy Stack****SciPy – The SciPy Stack****Pandas – The SciPy Stack****Java – Oracle****SAS – SAS Institute****MATLAB – MathWorks**

#### Databases software:

**MySQL – Oracle****PostgreSQL – The PostgreSQL Global Development Group****Cassandra – The Apache Software Foundation****MongoDB – MongoDB, Inc****CouchDB – The Apache Software Foundation**

#### Visualization and reporting software:

**D3.js – Mike Bostock****Tableau – Tableau Software****QlikView – QlikTech****R Markdown – RStudio**

#### Big Data software:

## 8. Data Science datasets

**Data.gov – U.S. Government****Open Data Census – Open Knowledge Foundation****Knowledge Discovery in Databases Archive – University of California****Quandl Financial and Economic Data – Quandl**

## 9. Data Science influencers

**Hilary Mason – Twitter, Website****DJ Patil – Twitter, Linkedin****Peter Skomoroch – Twitter, Website****Ryan Rosario – Twitter, Website****Andrew Ng – Twitter, Website**

## 10. Data Science formal education

**Master of Science in Data Science – New York University****Data Science Bootcamp – Zipfian Academy****Data Science Bootcamps – Metis**

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