The first section is an intentionally brief, functional, data science centric introduction to Python. The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible.
The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning and Linear Optimization to build systems and commandline tools.
- Lesson1: Introductory Concepts
- Lesson2: Functions
- Lesson3: Control Structures
- Lesson4: Intermediate Topics: Classes, Modules, Libraries
- Lesson5: IO in Python
- Screencast: How to setup a Python Project in Github, Test it with Pytest, use Pylint and Build it With CircleCI
- Screencast: How to launch AWS Spot Instances and Create Custom AMIs
- Screencast: How to use AWS S3 including from Pandas and Boto inside Jupyter
- How Create a Python Project Github Repository
- How to Write "Clean" Code in Python (2010) Using Pylint
- How to Test Jupyter Notebooks with Pytest
- How to build and test a Python Project with CircleCI
- How to get test Coverage with Pytest
- How to use Pylint to Fail on Error and Warnings only
- IBM Developerworks: Writing Multi-Threaded Programs in Python (2008)
- IBM Developerworks: Using Multi-processing Module in Python (2009)
- Writing Async Network IO Calls to AWS API
- Worker Farm with RabbitMQ and Tornado
- AWS + Boto: Python and AWS Jupyter Notebook
- AWS + Boto: Launching Spot Instances From Python
- AWS + Boto: Calling Spot Instance API to Create CLI Machine Learning Tool
- AWS + Boto: Spot Price Jupyter Notebook Exploration
- DEVML: Datascience around Github
- Social Power NBA: Datascience around the NBA and Social Media
- Spot Price ML(KMeans Unsupervized Machine Learning Recommender): Datascience around AWS Spot Prices
- Python Commandline tool Rosetta: Comparing R, Bash, Go, Node, Python and Ruby
- Pyli: Deduplication Commandline Tool That Walks A Filesystem
- Developersworks Article (2008): Creating Commandline Tools Python