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

Related link: The Data Science Toolkit

Other interesting links

• Data Science Certification
• Update about our Data Science Apprenticeship
• Our Wiley Book on Data Science
• Data Science Articles
• Our Data Science Weekly Newsletter