Workshop materials created for introduction into Data Science and programming languages

**This repository is currently under construction**

Teaching Repository

Author: Alex Nakagawa
This is a repository where all of the workshop and curriculum materials I have created reside. Most of these are in the form of Jupyter Notebooks and PDFs. You are free to do whatever you like with the materials that are in here.


[[decal-sql-lecture](https://github.com/alexnakagawa/teaching/tree/master/decal-sql-lecture)] - Introduction to PostgreSQL, SQL, and Relational Database Management Systems for the "Practical Data Science Skills for Internships" lecture series.
[[dss-intro-ml-workshop](https://github.com/alexnakagawa/teaching/tree/master/dss-intro-ml-workshop)] - Introduction to Machine Learning, with a focus on case studies, and how data scientists follow the data science lifecycle in the industry to approach certain problems.
[[dss-intro-r-workshop](https://github.com/alexnakagawa/teaching/tree/master/dss-intro-r-workshop)] - Introduction to using the R statistical programming language for data science. Created in collaboration with Junseo Park.
[[dss-python-ii-workshop](https://github.com/alexnakagawa/teaching/tree/master/dss-python-ii-workshop)] - Complete crash course on Python syntax with introductions to numpy and pandas for data science. Created in collaboration with Neelesh Dodda.
[[erg190c-hw8](https://github.com/alexnakagawa/teaching/tree/master/erg190c-hw8)] - Statistical analysis of environmental/geospatial data, CS189 topics.

External Teaching Resources

[[Data Science Modules Textbook](https://ds-modules.github.io/modules-textbook/intro)] - Lead an effort to consolidate Data Science Modules developed at UC Berkeley, co-edited by Shalini Kunapuli and Chris Pyles
[[PH 241: Statistical Analysis of Categorical Variables](https://github.com/PH241/materials)] - Graduate Course on Epidemiology. Created in collaboration with Nolan Pokpongkiat