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Originally posted here, where you can read comments and the most recent version.

We are now at 20, up from 17. I hope I find the time to write a one-page survival guide for UNIX, Python and Perl. Here's one for R. The links to core data science concepts are below - I need to add links to web crawling, attribution modeling and API design. Relevancy engines are discussed in some of the tutorials listed below. And that will complete my 10-page cheat sheet for data science. 

Here's the list:

  1. Tutorial: How to detect spurious correlations, and how to find the ...
  2. Practical illustration of Map-Reduce (Hadoop-style), on real data
  3. Jackknife logistic and linear regression for clustering and predict...
  4. From the trenches: 360-degrees data science
  5. A synthetic variance designed for Hadoop and big data
  6. Fast Combinatorial Feature Selection with New Definition of Predict...
  7. A little known component that should be part of most data science a...
  8. 11 Features any database, SQL or NoSQL, should have
  9. Clustering idea for very large datasets
  10. Hidden decision trees revisited
  11. Correlation and R-Squared for Big Data
  12. Marrying computer science, statistics and domain expertize
  13. New pattern to predict stock prices, multiplies return by factor 5
  14. What Map Reduce can't do
  15. Excel for Big Data
  16. Fast clustering algorithms for massive datasets
  17. Source code for our Big Data keyword correlation API
  18. The curse of big data
  19. How to detect a pattern? Problem and solution
  20. Interesting Data Science Application: Steganography

Other Cheat Sheets

Vincent's Cheat Sheets for Perl, R, Excel (includes Linest, Vlookup), Linux, cron jobs, gzip, ftp, putty: coming soon

Cheat Sheets for Python 

Cheat Sheets for R 

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