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16 changes: 8 additions & 8 deletions README.md
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# Python Deliberate Practice
# Python Deliberate Practice:

First of all, don't be afraid, read [Plateau of Productivity]. More importantly, be patient, a good read from Peter Norvig, titled [Teach Yourself Programming in 10 years].

## Motivation
## Motivation:

[Language war] between Python and R is one of the most frequently discussed topics among the Data Scientists, and there doesn't seem to be a consensus on which one is better. Personally, I used both R and Python, but for very different purposes. I mainly use tidyverse packages (dplyr + ggplot2) to carry out data analyses and data visualization, while using Python for web scraping, task automations, and building [basic web applications in Flask].

Expand All @@ -15,7 +15,7 @@ To me, the appeal of Python is not necessarily the Data Analysis part, R is alre

Here is a great [reddit answer] that explains the intersection and disjoint union of the two languages beautifully.

## Deliberate Practice
## Deliberate Practice:

I am a huge believer in learning by doing, and there are a lot of opportunities on the job where I can hone my Python skills through Deliberate Practice:

Expand All @@ -33,22 +33,22 @@ I am a huge believer in learning by doing, and there are a lot of opportunities

* **Immediate Feedbacks**: We have a culture of code reviews, both for IC work as well as internal package work. The former is harder because most DS on our team are in the R camp. There's also the weekly Python office hours that should be very useful. Find constant opportunities to get feedback as much as you can.

## Performance Goals
## Important Performance Goals:

* **[Immediate]** Learn to write pythonic code
* **[Shorter term, easiest to practice]** Write re-usable, modular, tested code for my data work and knowledge posts
* **[Medium term, harder to practice]** Achieve efficiency and feature parity on Data Analysis using Python compared to R
* **[Longer term, hardest to practice]** Write tools. Being able to work on projects that span the entire data stack using Python, apply good software engineering principles to these projects

## Project Goals
## Project Goals:

* **Outcome**: I want to move my data stack to Python completely. This means my day-to-day data analysis work will be done in Python instead of R, make my code as pythonic as possible. Become a Contributor to Airpy / tools, and take on one bigger Python project (ML, Data Viz ...etc).

* **Curriculum**: I want do everything that I can to go through all the basic materials in Pandas/Matplotlib combo. Expose myself to functional programming, OOP, testing in Python, or even making command tools. Get feedbacks from Airpy team members.

* **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.

## Project Milestones
## Project Milestones:

* **Learning Python & Best Practices**
* [Build On Top of the Basics: Python Progression]
Expand Down Expand Up @@ -84,7 +84,7 @@ I am a huge believer in learning by doing, and there are a lot of opportunities
* [BIDS: Python Bootcamp: Intro to Numpy]
* [Stanford ICME 193: Scientific Python]

* Introduction to Pandas
* Introduction to Pandas:
* [Dplyr/pandas Vignette Comparison]
* [Data School Pandas Tutorials]
* [Data School Pandas Github iPython notebook]
Expand Down Expand Up @@ -248,4 +248,4 @@ Once mastered all the above, the next natural step is to create public work that
[Berkeley BIDS Python bootcamp]:https://bids.berkeley.edu/news/python-boot-camp-fall-2016-training-videos-available-online
[Josh Bloom's Python Computing For Data Science]:https://github.com/profjsb/python-seminar
[Pandas Cookbook]:http://pandas.pydata.org/pandas-docs/stable/cookbook.html
[Udemy course]:https://www.udemy.com/learning-python-for-data-analysis-and-visualization/?ccManual=&couponCode=DEAL19
[Udemy course]:https://www.udemy.com/learning-python-for-data-analysis-and-visualization/?ccManual=&couponCode=DEAL19