- What is Data Science?
- How do I become a Data Scientist?
- How does Data Science differ from traditional statistical analysis?

## Related Courses

- Concepts in Computing with Data, Berkeley
- Practical Machine Learning, Berkeley
- Artificial Intelligence, Berkeley
- Visualization, Berkeley
- Data Mining and Analytics in Intelligent Business Services, Berkeley
- Data Science and Analytics: Thought Leaders, Berkeley
- Machine Learning, Stanford
- Paradigms for Computing with Data, Stanford
- Mining Massive Data Sets, Stanford
- Data Visualization, Stanford
- Algorithms for Massive Data Set Analysis, Stanford
- Research Topics in Interactive Data Analysis, Stanford
- Data Mining, Stanford
- Machine Learning, CMU
- Statistical Computing, CMU
- Machine Learning with Large Datasets, CMU
- Machine Learning, MIT
- Data Mining, MIT
- Statistical Learning Theory and Applications, MIT
- Data Literacy, MIT
- Introduction to Data Mining, UIUC
- Learning from Data, Caltech
- Introduction to Statistics, Harvard
- Data-Intensive Information Processing Applications, University of Maryland
- Dealing with Massive Data, Columbia
- Data-Driven Modeling, Columbia
- Introduction to Data Mining and Analysis, Georgia Tech
- Computational Data Analysis: Foundations of Machine Learning and Da..., Georgia Tech
- Applied Statistical Computing, Iowa State
- Data Visualization, Rice
- Data Warehousing and Data Mining, NYU
- Data Mining in Engineering, Toronto
- Machine Learning and Data Mining, UC Irvine
- Knowledge Discovery from Data, Cal Poly
- Large Scale Learning, University of Chicago
- Data Science: Large-scale Advanced Data Analysis, University of Florida
- Strategies for Statistical Data Analysis, Universität Leipzig

## Related Workshops

- Data Bootcamp, Strata 2011
- Machine Learning Summer School, Purdue 2011
- Looking at Data

## Books

- Competing on Analytics
- Analytics at Work
- Super Crunchers
- The Numerati
- Data Driven
- Data Source Handbook
- Programming Collective Intelligence
- Mining the Social Web
- Data Analysis with Open Source Tools
- Visualizing Data
- The Visual Display of Quantitative Information
- Envisioning Information
- Visual Explanations: Images and Quantities, Evidence and Narrative
- Beautiful Evidence
- Think Stats
- Data Analysis Using Regression and Multilevel/Hierarchical Models
- Applied Longitudinal Data Analysis
- Design of Observational Studies
- Statistical Rules of Thumb
- All of Statistics
- A Handbook of Statistical Analyses Using R
- Mathematical Statistics and Data Analysis
- The Elements of Statistical Learning
- Counterfactuals and Causal Inference
- Mining of Massive Data Sets
- Data Analysis: What Can Be Learned From the Past 50 Years
- Bias and Causation
- Regression Modeling Strategies
- Probably Not
- Statistics as Principled Argument
- The Practice of Data Analysis

## Videos

Source: http://datascienc.es

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