Gartner's Magic Quadrants as a source for what technologies to study

As a Data Scientists you need to learn a lot. Even if you know a lot you need to learn more. New things and don't stop learning, because you wanna be top. And people on top are at least know about trends if not following them and create them (that's the best choice :-)).

So, the idea is simple, follow up a Gartner's Magic Quadrants which shows all necessary players from specific field and look what is necessary to know, what should be fine and what is optional or even could be omitted. Also we should not forget to see who is novice player and who has been rejected.

Update 4/22/2014: Included pictures of all Gartner's Magic Quadrants mentioned in this post.

Reading: Free online resources March 2014

Last time I promised Hadoop books, but think is there (in the internet) are another books which make bigger buzz:

• The LION Way: Machine Learning plus Intelligent Optimization by Roberto Battiti and Mauro Brunato
• An Introduction to Random Forests for Beginners
• Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman and Jeff Ullman
• Ethics of Big Data by Kord Davis, Doug Patterson
• R Tutorial with Bayesian Statistics Using OpenBUGS by Chi Yau

So, next time maybe little bit more statistics and will see what I will find out interesting...

Course: Data Scientist Enablement Program (DSE)

Data Scientist Enablement Program (DSE) is something which I have found by accident. At the beginning of year I was looking for new courses or some complex study with topics about Data Science and Big Data and have been really keen to learn something new. Then I found form which gave me access to SONO to DSE.

This program and its all 4 modules (so far I have done 1st one, so I am just assuming based on the current experience) are not classic course with video tutorial, quizzes and labs with question form and score. It is almost like in school, you can participate or you can not, you can read and watch materials which you got, but nobody checks if you have done it or not. Only one thing you need to do, is submission of assignment. And even this action is not time limited, so there is no penalty for late submissions, only recommendation to do it ASAP.

Moreover, you will get certificate corresponding to your maturity level in this study and based on your composite score which is computed based on your social engagement, activities, projects, collaboration, etc in all 4 modules.

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.