Tools in Data Science - May 2026#
This is the course readme for the May 2026 term of Tools in Data Science .
Tools in Data Science is a practical course about building, deploying, evaluating, and communicating data science work. The course focuses on working tools and reproducible workflows rather than only concepts. Students should expect to use command-line tools, Python, GitHub, APIs, LLMs, data processing tools, and visualization workflows throughout the term.
Teaching Team#
| Role | Name | Notes |
|---|---|---|
| Course instructor | TBD | Add the confirmed instructor details before the term begins. |
| Teaching assistants | TBD | Add TA names, contact routes, and support responsibilities here. |
| Course support | TBD | Add forum, helpdesk, or escalation details here. |
Learning Outcomes#
By the end of the course, students should be able to:
- set up and maintain a reproducible development environment for data science work
- use command-line tools, Git, GitHub, and structured text formats confidently
- deploy data science outputs as websites, APIs, scheduled jobs, or lightweight services
- use AI coding tools with review, testing, and reproducibility discipline
- build LLM-based workflows using prompting, structured outputs, retrieval, and evaluation
- source data from APIs, websites, documents, and public datasets responsibly
- clean, transform, validate, and analyze data using appropriate tools
- communicate results through charts, dashboards, narratives, and presentations
Course Structure#
The May 2026 course content opens with a short Foundation Ramp and is then organized into eight weeks. The sidebar keeps each week collapsed by default; expand a week to see the topics covered.
| Week | Focus |
|---|---|
| Ramp | Foundation Ramp — setup before Week 1 |
| 1 | Development Environment & Tooling |
| 2 | APIs, Data Retrieval & Simple Apps |
| 3 | AI, LLMs, Prompting, Context & Cost |
| 4 | Search, Embeddings & Practical RAG |
| 5 | Coding Agents, Skills, Hooks & Browser Automation |
| 6 | Web, Documents & Multimodal AI |
| 7 | AI Safety, Security, CI/CD & Cloud Deployment |
| 8 | Portfolio, MLOps Lite & Future Readiness |
Assessments#
- Project 1 appears after Week 3.
- Project 2 appears after Week 6.
- Project 3 is the final project at the end of the course.
- ROE is the remote online exam held at the end of the term.
- End Term appears after Week 8.
Important Links#
Repository Guide#
All new May 2026 course content should live inside 2026-05/. Each week and project is a
folder with its own README.md plus lesson/topic pages.
2026-05/
├── README.md
├── _sidebar.md
├── assets/
├── foundation/ # README.md + setup pages (before Week 1)
├── week1/ … week8/ # README.md + one page per topic
├── project-1/ # README.md, project-description.md, project-requirements.md
├── project-2/ # README.md, project-description.md, project-requirements.md
├── project-3/ # README.md, project-description.md, project-requirements.md
├── roe/ # README.md
└── resources/ # system requirements, course links, live sessions, assessmentsOlder terms remain in their term folders as archives. They should not be mixed into the current sidebar.