Advanced Data Science for Traffic and Transportation Engineering

This project-based course is offered in close collaboration with companies (CO) and governmental organizations (GO), ensuring that student projects are grounded in real-world challenges currently faced by the transport and logistics sector.

Over the course of 8 weeks, students will develop foundational knowledge and practical skills across the full data pipeline, including data processing and analytics to visualization. These skills are directly applied to a group project based on either an assigned or chosen project. Each project is aligned with a specific challenge proposed by one of the participating external stakeholders.

To support the project development, weekly thematic sessions and lab exercises are provided. Topics include:

• Data Processing and Visualization

• Data Analytics and Feature Engineering

• Machine Learning Methods

Additionally, students will be given access to self-learning materials to further deepen their understanding.

The course includes lectures on agile project management principles, which students are expected to apply to plan and execute their project tasks effectively. Working collaboratively in groups, students will explore and analyze real datasets to extract actionable insights for the related stakeholders. Throughout the course, they will also have the opportunity to engage in dialogue with industry experts to refine their approach and gain practical perspectives.

A key deliverable in the project is a reflection and proposal on how their solution contributes to the stakeholders. Students are expected to formulate concrete recommendations or a scalable design that outlines how their prototype could be further developed for real-world implementation, which can be finetuned in collaboration with stakeholders.

Note: A minimum of 4 students is required for the course to be offered, ensuring at least one functioning project group.

Exam: N/A.

Assessment procedure:

The assessment of this course is based on the output of the group project, which includes data story, code base, and final presentation. Students will receive a single group grade, with possible individual adjustments based on peer and instructor evaluations to account for differences in individual contributions. To successfully complete the course, students must achieve a minimum grade of 5.8.

A repair option is available only for students who receive a failing grade (<5.8) for their project. In this case, students may resubmit an improved version of their project based on feedback.
The maximum grade that can be obtained through the repair process is 5.8.

For more information on grading, see article 14 in the Rules and Guidelines (RGBE):

https://www.tudelft.nl/en/student/ceg-student-portal/education/education-information/educational-rules-and-regulations