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.

Course: Data Science Specialization on Coursera

Surprise came to my mailbox today morning. Coursera announced new type of study: Specialization.

Specializations are new multi-course programs. In case of Data Science Specialization you will take 9 separate courses with signature track for $49 and earn certificate. And complete track you can finish with capstone project which is another $49. So, if you wanna be certified for Specialization you need finish 9 certified courses and do final project. In total you will pay $490, but it is in 10 separated payments. Great thing is all of these courses are under patronage of Johns Hopkins University which I hope ensures quality. I need to say that based on my experience with Computing for Data Analysis course which is done by this university as well, it has really good quality!

Course: Computing for Data Analysis

When I signed to course Computing for Data Analysis on Coursera (since mid of 2nd week I am doing Signature track which is more challenging). I did not expect that it should be so time consuming and so tough. It is. But this strictness doesn't allow me to go just over the surface, I need to go deep down and really learn R language. Which I actually good. I am lazy from nature, but who isn't :-).

Looking for first hands on experience

Last week I was quite busy with MongoDB course. And even though I have plenty work with final exam which contains 10 separate works I am little bit fed up of databases at all. No matter if NoSQL or SQL which I work with really intensively in last week before Christmas.

I moved my sight to the programming language and was looking for some practical hands on experience. I have been lucky and found Data Mining: Discovering and Visualizing Patterns with Python, when I was looking for new articles and resources about data science. When I found this I was so excited! It's like kill many data science skills with one stone. I can play with Python instead of the database, work with new data and visualize it. Good, let's see how it works.