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