I recently finished the first two modules of Arcitura's Big Data School. Consequently, I am now certified as a big data professional. Yay me!

The first two modules cover the definition and scope big data and big data systems architecture at a high-level and in a platform-agnostic way. I appreciate the platform agnosticism. The modules take a few hours to go through before being ready for the exam. I found that if I read through the workbook twice, I was more than adequately prepared for the exam and was able to pass them both with honors. Also, the included CDs seem pretty useless unless you are a particularly aural learner. I found the tone of the speaker to be dull and uninteresting. The books that come with the modules seem interesting -- I only read half of the first one and haven't started the second one. They are not necessary for the exam however.

I plan to continue with modules 4-6 until I earn my Big Data Scientist certification. 

Last week, I finished plowing through Allen Downey's Think Stats. It took me about 5 days to work through it cover-to-cover. This book is great for those who need a crash course in using Python to do statistics. The conceit of the book is that it's a stats book for programmers. Hence, the statistics in it aren't past an upper-lever undergraduate level in difficulty. However, the programming required to work through the problems is at a higher level than I have seen in any other introductory stats book. I found it to be a great way to improve my Python programming. I worked through about 80% of the examples, skipping only those that were either trivial or did not seem relevant to my learning goals. For many of the examples, Downey provides .py files you can download from the book's website. I found that I usually took a different approach to solving problems than he did, but we most often agreed on the solutions. 

Anyway, the book is available as a free download and worth the time.