Welcome! The purpose of this repository is to serve as stockpile of statistical methods, modeling techniques, and data science tools. The content itself includes everything from educational vignettes on specific topics, to tailored functions and modeling pipelines built to enhance and optimize analyses, to notes and code from various data science conferences, to general data science utilities. This will remain a work in progress, and I welcome all contributions and constructive criticism. If you have a suggestion or request, please use the "Issues" tab and/or reach out to me via Twitter, and I will respond expeditiously!
Note: GitHub often has trouble rendering larger .ipynb files in particular. If you find that you are unable to view one of the jupyter notebooks linked below, I recommend copy and pasting the result into jupyter's nbviewer, which will take you to a viewable link like this one here for my "Visualization with Plotly" notebook. Note that if you want to ensure that you are viewing the most up-to-date version of the notebook with nbviewer, you should add
?flush_cache=true to the end of the generated URL as is described here; otherwise, your link risks being slightly out-of-date.
Table of Contents
- Playground and Basics
- Exploratory Data Analysis (EDA) and Visualization
- Hypothesis Testing
- Reinforcement Learning
- Text Mining and Natural Language Processing (NLP)
- Time Series
- Notes and Material from Data Science Conferences
All are welcome and encouraged to contribute to this repository. My only request is that you include a detailed description of your contribution, that your code be thoroughly-commented, and that you test your contribution locally with the most recent version of the master branch integrated prior to submitting the PR.