17 short tutorials all data scientists should read

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:

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

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