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What Software Engineering Principle Do You Want To Learn Through Python?

Estimate Time Required To Achieve Goal

I will give myself the whole 2016-Q4 to work on this goal. It seems like a long time, but effectively, I have only 8 weeks left (as of 2016-10-08) because of Open, Thanksgiving, and Christmas.

Timeframe: Efficiency parity by end of October. One contribution to Airpy by Mid November. One ongoing big project touching different stacks in Python by the end of 2016.

Break Down Work Into A Week-by-week Schedule

  • Week of 8/8 - 8/14: Start watching Stanford CS 41
  • Week of 8/15 - 8/20: Finished Stanford CS 41 + started Data School Pandas videos
  • Week of 8/21 - 8/28: Detour to watch Tom Augspurgur's Pydata video
  • Week of 8/29 - 9/4: Birthday week
  • Week of 9/5 - 9/11: Start reading Idiomatic Python + Finished Data School Pandas videos series
  • Week of 9/12 - 9/18: Start reading "Python Data Analysis" by Wes Mckinney
  • Week of 9/19 - 9/25: Start practicing Python on the job (calculating cohort size from lead ranking)
  • Week of 9/26 - 10/2: Continue to practice Python on the job (variable transformation model) + Data School Machine Learning videos. This is one of the best examples of learning on the job with Python.
  • Week of 10/3 - 10/9: Built a simple data visualization of demand index using Python's Bokeh library + Finished Data School ML videos
  • Week of 10/10 - 10/16: Continue to play with Bokeh, made slider (interactivity) worked, and made animation of world demand viz worked. A bit reading on command line in Python
  • Week of 10/17 - 10/23: Practice with writing a simple Python parser for excel to get event annotation. Made timeseries and interactivity worked. Started reading Python OOP articles
  • Week of 10/24 - 10/30: Simeon Franklin's OOP class - first part
  • Week of 10/31 - 11/4: Simeon Franklin's OOP class - second part (less useful)
  • Week of 11/7 - 11/11: BIDS video on OOP + Coursera's Introduction to DS in Python Class + Chris Albon's notes + Heroku Deployment of Airbnb-open-data-viz app
  • Week of 11/14 - 11/18: (Airbnb Open - Half week) Start building First Stage Model for LTV project in Python + Pandas Plotting Documentation + BIDS: Scipy Pandas + Scientific Workflow in Python (both are marginally helpful)
  • Week of 11/21 - 11/25: (Thanksgiving - Half week) On the Job learning for scikit-learn (pipeline, learning curve) + BIDS matplotlib review + Data School: writing pythonic code + Test Driven Development for Scientist blog post
  • Week of 11/28 - 12/2: BIDS Ipython notebook, BIDS functions/modules, BIDS: advanced string, BIDS testing driven development, Jeff Knupp's Unit Testing, Software Carpentry on Testing with Python
  • Week of 12/5 - 12/9: BIDS Numpy, BIDS Advanced Interaction, Simeon Franklin's higher order function, Jeff Knupp's "improve your python series": yield/generator, decorator, drastically improve python understanding of execution code, loop like a native (really good)
  • Week of 12/12 - 12/16: During the weekend (12/9), add more newer topics (logging, command-line tool, and building packages) for 2017. No active learning for this week, but wrap up by reviewing materials for a blog post