sridharavulapati

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
• 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