Analytics or data science has following components:
- data mining/machine learning/statistics
- data visualization
- database management
- programming
There are some free online courses that cover many of these areas, and these courses are usually part of a degree or a certificate program in data mining. Those who are new or interested in this field can learn a whole lot without paying a dime. Here is the list:
- Intro to Probability and Statistics (Carnegie Mellon)
- Machine Learning 101/102
- GovData (MIT/Harvard)
- STATS 120: Information Visualisation (The University of Auckland)
- R Programming (UCLA)
- CS 229: Machine Learning (Stanford) (videos)
- Linguistics 420: Statistical Natural Language Processing (Georgetown)
- SI 508: Networks: Theory and Application (University of Michigan)
- CS 591: Data Mining (West Virginia University)
- STATS 782: Computing for Statisticians (The University of Auckland)
- 6.867: Machine Learning (MIT)
- Andrew Moore’s Slides on Statistical Data Mining Tutorials
- Lots of tutorials (Data Mining Tools)
- Capstone project: kaggle or kdd (for a bigger list see kdnuggets)
Some free text books:
- The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
- Mining of Massive Datasets by Rajaraman and Ullman
In addition, there is an excellent thread on quora on how to become a data scientist that covers lot of things and is a very good resource on the practice of analytics.

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