Reading: Free online resources February 2014

The new package of brain-food from February. Last time it was about intro to Data Science, Statistics, some ML and Analysis. This time it is again about Statistics, ML and Analysis and of course available for free and online:

• An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• Machine Learning for Hackers by Drew Conway, John Myles White
• OpenIntro Statistics, Second Edition by David M Diez, Christopher D Barr, Mine Cetinkaya-Rundel
• Think Bayes: Bayesian Statistics Made Simple by Allen B. Downey
• Python for Data Analysis: Agile Tools for Real World Data by Wes McKinney

Next time Hadoop and related staff, but not only by Hadoop is live a men ;-).

Study: Apply to Berkeley School of Information MIDS Program

It was little bit easygoing decision. When I saw offer to apply to Berkeley School of Information Master of Information and Data Science (MIDS) Program I tried it. It is completely online study. And here is my experience with how it went. In front of this post I need to say I haven't finish it and I won't do it. The main reason is: for me it is impossible to finance it. Before you start, please have a look over financial part too. You can find tuition-fee described in detail in separate article. I haven't done that, I expect it is expensive, but not so much, of course it is Berkeley :-), so I have been surprised in the middle of application.

Course: M101P - MongoDB for Developers

M101P: MongoDB for Developers in Python language course was really good experience with hands on practice and many programming staff around. I need to thanks to Andrew Erlichson for his really big effort which he includes into this course at MongoDB University. I've learned a lot and I started to like NoSQL databases at least this one.

Reading: Free online resources January 2014

This is just a simple list of sources which I have found on different places and are available free and online:

• An Introduction to Data Science by Jeffrey Stanton
• Introduction to Probability and Statistics Using R by G. Jay Kerns
• R for Machine Learning by Allison Chang
• Think Stats: Probability and Statistics for Programmers by Allen B. Downey
• Data Analysis with Open Source Tools by Philipp K. Janert

I will post new list, when I have something new, if possible next month.