Applied Computing in Health Data Science

Companion repository for PUBH 6854 / PUBH 4201, Fall 2026 (Matthew Bendall, GWU Milken Institute School of Public Health).

Application of computational paradigms in biomedical sciences and health informatics: computing environments, reproducible research, data management, and analysis workflows using R, Python, and generative AI.

Deadlines

All deadlines are at 11:59pm (ET) unless otherwise noted.

Assignment	Due Date
Quiz 1	Fri, Sep 4
Lab 1 — Reproducible Computing Setup	Wed, Sep 9
Lab 2 — Analysis Notebook	Wed, Sep 16
Quiz 2	Fri, Sep 25
Lab 3 — Parsing Messy Health or Genomic Data	Wed, Sep 30
Final Project Proposal	Wed, Oct 7
Quiz 3	Fri, Oct 9
Lab 4 — Database-Driven Feature Table	Wed, Oct 28
Quiz 4	Fri, Oct 30
Lab 5 — Scalable Analysis Workflow	Wed, Nov 4
Quiz 5	Fri, Nov 20
Final Project Presentations	Mon–Wed, Dec 7–9
Final Project (final deliverable)	Mon, Dec 14

Office Hours

I will typically hold office hours 2:30 - 3:30 on Monday or Wednesday (location TBD).

Schedule (subject to change):

Day	Time	Last chance before
Wed Sep 2nd	2:30 - 3:30
Wed Sep 9th	2:30 - 3:30	Lab 1 due 9/9
Mon Sep 14th	2:30 - 3:30	Lab 2 due Wed 9/16
Wed Sep 23rd	2:30 - 3:30
Mon Sep 28th	2:30 - 3:30	Lab 3 due Wed 9/30
Wed Oct 7th	2:30 - 3:30	Final project proposal due 10/7
Mon Oct 12th	2:30 - 3:30
Wed Oct 21st	2:30 - 3:30
Mon Oct 26th	2:30 - 3:30	Lab 4 due Wed 10/28
Wed Nov 4th	2:30 - 3:30	Lab 5 due Wed 11/4
Mon Nov 9th	2:30 - 3:30
Wed Nov 18th	2:30 - 3:30
Wed Dec 2nd	2:30 - 3:30
Mon Dec 7th	2:30 - 3:30

Additional office hours are by appointment.

Repository structure

• lectures/ — one folder per week (week01…week15), slides and in-class materials
• labs/ — the 5 graded lab assignments; see labs/README.md for the full list, due dates, and submission instructions shared by all labs
• project/ — final project requirements, proposal template, rubrics, example project ideas
• environment/ — environment/dependency files (conda, renv, Docker) for the course
• setup/ — one-time computer setup instructions, by OS (SETUP.md is the entry point)
• data/ — raw/ (input datasets used in labs/lectures) and processed/
• resources/ — supplemental readings and slide assets

Course structure

15-week semester in 3 modules:

1. Weeks 1–5 — Computing Environments, Reproducibility, and Data Wrangling
2. Weeks 6–10 — Programming, Databases, and Scientific Visualization
3. Weeks 11–15 — Generative AI, Prompt Engineering, and Advanced Workflows

See SCHEDULE.md for the full session-by-session schedule and GRADING_AND_POLICIES.md for grading breakdown, evaluation criteria, and class policies (collaboration, generative AI use, late work, etc.). See project/requirements.md for final project details and the syllabus for the authoritative, official version of policies and grading.

Setup

One-time computer setup (terminal, git, conda/mamba, uv, R/renv, Docker, an IDE) lives in setup/SETUP.md, split out by OS (Windows, macOS, Linux). The course's own reference environment files (used to build/test lecture demos and lab solutions) are in environment/.

License

Code is licensed under MIT. Lecture slides, notes, text, images, and video are licensed under CC BY-SA 4.0.