Overview

Learning Goals

Throughout the course, you work on different phases of the data preparation and workflow management pipeline. In the team project, you bring these components together.

Together with your team members, you will set up a reproducible workflow on GitHub, apply the end-to-end Git workflow for versioning files and manage your project using GitHub Issues, and build and automate a data preparation and analysis pipeline from scratch.

Importantly, this project may be different from other projects in your program. The goal is not to write an academic research paper. Instead, the goal is to build the infrastructure around one: clean workflows, clear versioning, reproducible automation, and collaborative project management. For details, see the grading guidelines.

Organization

Data

All teams receive TikTok data with realistic (simulated) watch logs for thousands of users. Part of the data is shared as CSV files, and part is shared as a SQL database. See Project Data for details.

Deliverables

Students work in one GitHub repository. Each week, you develop the project along four dimensions: project management, coding, versioning, and automation.

  • Week 2 (Team work): Explore one selected CSV file and create a clean Quarto summary.
  • Week 3 (Individual work): Build visualization outputs from assigned tasks.
  • Week 4 (Individual work + Team work): Revise individual contributions and complete team integration through pull requests.
  • Week 5 (Team work): Extend the project with analytics and advanced data-preparation techniques, and integrate database input.

See the workplan and coaching page for the week-by-week requirements.

Grading

  • The project grade combines team work components (65%) and individual work components (35%).
  • Week 4 is split: project management, coding, versioning, and automation are assessed individually, while collaboration/integration is assessed as team work.
  • No self- or peer-assessment score adjustment is used.
  • There are four deliverable moments in Weeks 2-5, each submitted as an updated GitHub repository.
  • Deliverables for Weeks 2-4 are due on the day before the next coaching session; Week 5 has no additional deliverable due before Coaching #5.
  • Exact date/time deadlines and final submission details are specified on Canvas; the weekly rhythm is documented on the workplan and coaching page.

Coaching sessions

To support students, teams meet with an instructor to receive feedback on progress. This often includes troubleshooting and debugging.

  • All teams attend the full session. Teams collaborate on their project while the instructor provides support (in-person or through Zoom breakout rooms).
  • First half: Each team gets 5-10 minutes to share a progress update and ask targeted questions. Second half: Additional time is allocated based on team needs.

Permitted Level of AI Use

  • Weeks 2-3: AI-assisted idea generation and structuring (Level 3 on AI Index Tilburg University)
    • You are allowed to use generative AI tools to develop or refine initial ideas, materials, paraphrasing, structures, or outlines. This includes generating code, e.g., for R.
    • Failing to declare AI use, or using AI beyond what is allowed in the syllabus, may be considered fraud and will be reported to the Examination Board.
  • Weeks 4-6: AI task completion, human evaluation (Level 4 on AI Index Tilburg University)
    • You are allowed to use the generative AI tool to complete certain elements of the task (e.g., literature search, data analyses, audio transcription), and you are responsible for critically evaluating its output to meet the assessment requirements.
Where to submit the project?

There is no separate report. The project should document itself through clear code, issue history, automation files (including Makefile), and a strong README.md that ties everything together.

Note that we will check out the state of the repository at the deadline date and time, so any changes you make to the repository afterwards are not considered for grading.