Course Overview

This is a collaborative, hands-on project course spanning 8 weeks during the spring semester (P4 period). The course aims to provide students with the opportunity to design, develop, and execute end-to-end data science and AI workflows while building essential research, technical, and teamwork skills. Students work in small teams of 4-5 under the guidance of academic mentors (PhD students, postdocs, and faculty) to tackle real-world data analysis challenges. At the end, we plan to organize a small student conference where teams can present their findings to peers and faculty.

Course Coordinators:

Learning Objectives

By the end of this course, students will be able to:

  1. Scope a problem: Translate a research/industry question into a well-defined, solvable data analysis problem.
  2. Develop and execute a pipeline: Design and implement a reproducible end-to-end data pipeline, including data cleaning, machine learning modeling, evaluation, and presentation.
  3. Collaborate: Use professional tools (e.g., Git) to manage collaborative team workflows.
  4. Communicate: Present technical results via a written scientific report and a conference-style presentation.

Projects are evaluated not based on model performance, but on the originality of ideas, the soundness of proposed approaches, and the depth and justification of the analysis. Students are encouraged to be creative and to explore new directions, regardless of the downstream performance of the proposed approach(es).

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Please fill out the form below to sign up as a supervisor and propose a project:

Note that, you will be prompted to log in to the institutional GitLab. Please use SSO login to do so!

Propose a project

The deadline for project proposals has passed

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