This repository serves as a resource hub where I’ve collected information, notes, and configurations that I found helpful during the Master of Science in Applied Data Science (MS-ADS) program at the University of San Diego. I hope this can also be useful for others currently enrolled in — or preparing to start — the program.
It includes setup guides, environment configuration notes, and course references that I found most useful throughout my studies.
🧠 Note: Some setup guides in this repository were refined or reformatted with the help of ChatGPT to improve clarity and organization. All technical content is based on my actual configurations and experiences during the program.
I used to develop primarily on a Mac but have since replaced my main PCs with Windows-based systems. For the most part, the MS-ADS program can be completed entirely in Windows.
However, in a few classes it’s necessary to run both Python and R in the same Jupyter Notebook — something that isn’t supported natively in Windows. Thankfully, Windows Subsystem for Linux 2 (WSL2) makes this possible.
Setup guides and references:
- Setting up WSL2 and VS Code in Windows
- Creating a Conda Environment to Run Python and R in Windows
- Exporting Jupyter Notebooks as PDF from VS Code
- Using Aliases in WSL2
- Creating and Updating Conda Environments from a YAML File
- Recommended VS Code Extensions for MS-ADS
- ADS 500A — Probability and Statistics for Data Science
- ADS 500B — Data Science Programming
- ADS 501 — Foundations of Data Science and Data Ethics
- ADS 502 — Applied Data Mining
- ADS 505 — Applied Data Science for Business
- ADS 506 — Applied Time Series Analysis
- ADS 507 — Practical Data Engineering
- ADS 508 — Data Science with Cloud Computing
- ADS 503 — Applied Predictive Modeling
- ADS 504 — Machine Learning and Deep Learning for Data Science
- ADS 509 — Applied Text Mining
- ADS 599 — Capstone Project
I began developing my own Exploratory Data Analysis (EDA) library to simplify repetitive EDA tasks that come up across multiple courses.
It’s still in active development, so updates may be infrequent, but it’s already functional for common EDA workflows.
You can check it out here:
👉 jcds repository
Once installed, the library’s functions can be imported directly for use within Jupyter Notebooks.
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