Which technology stack to use?

It is the phase of my startup when I am deciding the technologies we will be using to build the Online Athletigen service. It is so confusing to decide on one. There are so many great stacks I could fixate on but all of them have their strengths and weaknesses and there is so much advice to confuse me even more. I boiled down to Ruby on Rails or Python Django, at first. I was going to build a small sample app in Django first and then ROR. I told myself, If I stick with ROR, to start I am going to read the book, Programming with Ruby and then follow the free book online on Ruby on Rails. One of my teammates, Daniel has been insisting on FLASK micro framework, but I knew it is a one file micro framework so I didn’t give it serious thought. When I did, I discovered Pyramid from a reddit post. I was amazed. I read about the web frameworks war and comparisons. I installed VIRTUALENV and started my journey with Pyramid from this introduction. I viewed a presentation on what Pyramid had to offer. The power and advantages of Pyramid  what convinced me that Pyramid is worth the investment. So, SQLalchemy and Pyramid is what I am going forward with. I installed PyCharm, don’t know why I waited so long to experience its awesomeness. Now, I am feeling well equipped to dive into the project. And there is always something unexpected that happens in early stages of a project. The project development took a turn and the stack changed completely to SPRING MVC + AngularJS. This change was triggered by a new java enterprise developer who joined our team recently and was not comfortable with interpreted and not strictly OOP nature of Python.

Resources for Data Sceince

I created a mind map of the resources easily accessible for becoming a Data Scientist.

Mind MapIf the map is not clearly visible then here is a link on iMind http://imm.to/vQSjF, .

The links cited in the mind map are:

  1. http://scikit-learn.org/stable/presentations.html

  2. http://www.youtube.com/watch?v=w26x-z-BdWQ

  3. http://www.nytimes.com/2009/08/06/technology/06stats.html?_r=3&emc=eta1&

  4. http://www.youtube.com/watch?v=YLiwCKpoW1Q

  5. http://www.youtube.com/watch?v=p8hle-ni-DM

  6. http://www.youtube.com/watch?v=MxRMXhjXZos

  7. http://videolectures.net/mloss08_hunter_mat/

  8. http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf

  9. http://www.cs.ubc.ca/~nando/340-2009/lectures/linalg.pdf

  10. http://docs.python.org/2/tutorial/

  11. http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html

  12. http://www.cs.ubc.ca/~nando/340-2012/python.php

  13. http://www.quora.com/What-are-the-best-open-source-machine-learning-libraries-written-in-Python

  14. http://tryr.codeschool.com/

  15. http://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/lecture-notes/MIT15_097S12_lec02.pdf

  16. In the Data Analysis R section https://www.kaggle.com/wiki/Tutorials

  17. http://www.johndcook.com/R_language_for_programmers.html

  18. Prerequisite section http://mlthirst.wordpress.com/2012/02/20/video-resources-for-machine-learning/

  19. Basic ML http://mlthirst.wordpress.com/2012/02/20/video-resources-for-machine-learning/

  20. http://shop.oreilly.com/product/0636920018483.do

  21. http://www.amazon.com/Pattern-Classification-2nd-Richard-Duda/dp/0471056693

  22. Advanced ML http://mlthirst.wordpress.com/2012/02/20/video-resources-for-machine-learning/

  23. http://www.bioinformatics.org/wiki/Introduction_to_bioinformatics_(book_list)

  24. http://www.bioinformatics.org/wiki/Genomics,_genetics_and_related_sequence_analysis_with_computers_(book_list)

  25. http://www.ats.ucla.edu/stat/r/


This will be improved over time. But this is a good initial start for me to get the ground running. Please add feedback in the comments.