20 short tutorials all data scientists should read (and practice)
Vincent Granville
DataScienceCentral, 15 February 2014
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:
- Tutorial: How to detect spurious correlations, and how to find the …
- Practical illustration of Map-Reduce (Hadoop-style), on real data
- Jackknife logistic and linear regression for clustering and predict…
- From the trenches: 360-degrees data science
- 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
Read rest of article with other cheat sheets and links.
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