Functional, Data Science Intro To Python

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, Linear Optimization, build systems, commandline tools, recommendation engines, Sentiment Analysis and Cloud Computing.

Python Fundamentals

• Lesson1: Introductory Concepts
• Lesson2: Functions
• Lesson3: Control Structures
• Lesson4: Intermediate Topics: Classes, Modules, Libraries
• Lesson5: IO in Python

Screencasts (Can Be Watched from 1-4x speed)

• 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

Advanced Topics (In Progress....)

Software Carpentary: Testing, Linting, Building

• 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

Concurrency in Python

• 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

Cloud Computing-AWS-Sentiment Analysis

• AWS Sentiment Analysis Notebook

Recommendation Engines

• Hand coded tanimoto and Pandas similarity engine

Cloud Computing-Azure-Sentiment Analysis

• NLP on Azure

Cloud Computing-AWS

• 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

Cloud Computing-GCP

• Google Sheets to Pandas DataFrame
• BigQuery to Pandas to Seaborn
• NLP on Google Cloud

Machine Learning and Data Science Full Jupyter Notebooks

• 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

Data Visualization

Seaborn Examples

• Visualizing Spot Prices in Seaborn
• Correlation Heatmap in Seaborn for NBA

Plotly

• Exploring 30 years of Real Estate Prices with Zillow Data and Plotly

Creating Commandline Tools

• 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

Creating a complete Data Engineering API

• Real world Data Engineering API

Statically Generated Websites

• Interfacing with Hugo and Algolia from Python

Deploying Python Packages to PyPi

• Article on how to deploy commandline tools and libraries to PyPi

Web Scraping in Python

• Programatically Scraping Wikipedia API in Python: Pageviews and Articles

• Using Python Twitter API

• Serverless Web Scraping with Lambda and Step Functions

Logging in Python

• "Sensible" Logging Library in Python: Deployed to PyPI

Mathmatical and Algorithmic Programming

Optimization

• IBM Developerworks Series on Pyomo (Linear Optimization in Python)

• Traveling Salesman (NP Hard Simulation Solution with Random, Greedy Start)

• Greedy Coin Example in Python

Linear Regression

• Linear Regression in Jupyter Notebook