Big Data and Data Science study - presentation

During our Friday 26th of September company meeting I talked about topic in title with presentation. I think this presentation could be useful not only for audience which was there but for anyone.

Here is description what is content of presentation about:
Big Data and Data Science study with subtitle "study materials and online courses" is little bit more over 40 slides presentation about 10 domains of Data Science covered by online free and paid MOOC courses, study materials and free books.

Based on my almost year of study, investigate and collect of materials, tutorials, courses, books, links, etc. I have prepared distillation of the best in this short presentation.

Of course list is not full, because there is always something new, undiscovered and better than before. But it contains the most important information for those who want to start or don't know where exactly follow up when they already begun.

Follow up to the Speaker Deck site and you can download presentation as PDF which is quite useful when you consider functional links over all presentation.

Reading: Free online resources June 2014

I need to say, my last obsession is NLP. After I came through basics of ML/DM techniques I challenge my self in NLP Kaggle.com contest about Sentiment analysis. So, if you are interested about free (as usual of course) basic sources  for this topic, be my guest:

• Natural Language Annotation for Machine Learning by James Pustejovsky, Amber Stubbs
• Natural Language Processing with Python by Steven Bird, Ewan Klein, Edward Loper
• Python Text Processing with NLTK 2.0 Cookbook by Jacob Perkins
• Taming Text by Grant S. Ingersoll, Thomas S. Morton, Andrew L. Farris
• and many others...

Unfortunately I have found that 1st one is not completely available, just first 4 chapters, but as intro it is good enough.

Reading: Free online resources April 2014

So, more statistics and what next I found interesting (I really like D3!), here is the list:

• Learning Statistics with R by Daniel Navarro
• Mining the Social Web 2nd Edition by Matthew A. Russell
• Online free iPython notebook version
• Online free PDF version
• Getting Started with D3 by Mike Dewar
• R Graphics Cookbook by Winston Chang

Like I wrote in my previous blog post. My intention is to have more fun with Data Science then just being overloaded by homeworks, assignments and quizzes.

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...

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 ;-).

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.