EPA 1316 Introduction to Urban Data Science

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Welcome to Introduction to Urban Data Science (EPA 1316) at Delft University of Technology. The course is taught by Dr Trivik Verma.

Instructor Details

Dr Trivik Verma
Assistant Professor in Urban Science & Policy
B2.390, Building 31
Faculty of Technology, Policy and Management
Jaffalaan 5
2628 BX Delft
The Netherlands
E: t.verma [at] tudelft.nl


The schedule for the course is:

  • Lectures: Mon and Wed 1315-1430 and 0915-1030 respectively, Hybrid
  • Computer Labs: Wednesdays 1045-1300, Hybrid
  • Discussions + Presentations: Mondays 1445-1700, Hybrid


Course is hosted in a hybrid format with a mix of physical and virtual classrooms. All lectures, labs, discussions, and office hours will be hosted in a hybrid manner depending on the COVID-19 situation at the time (see below for more information).

  • Physical location information is mentioned in Brightspace calendars.
  • Virtual sessions are hosted Online @: BigBlueButton. Students need to use their TU Delft login details to join.
  • Announcements regarding the location and form of a session will be made on Brightspace on a regular basis.

Teaching Assistant Support

Teaching Assistant Email Role
Auriane Técourt A.C.Tecourt [at] student.tudelft.nl All
Luka Janssens L.M.I.Janssens [at] student.tudelft.nl All
Marya El Malki M.ElMalki [at] student.tudelft.nl All
Mobeen Nawaz M.F.Nawaz [at] student.tudelft.nl All
Stephan Olde S.A.Olde [at] student.tudelft.nl All
Ruth Nelson R.J.Nelson [at] tudelft.nl All
Dr Juliana Gonçalves J.E.Goncalves [at] tudelft.nl Discussion only

Course Language

English & Python

Why Python

  • General purpose programming language
  • “Sweet spot” between “proof-of-concept” and “production-ready”
  • Industry standard: GIS (Esri, QGIS) and Data Science (World Bank, OECD, The Atlantic, Gemeente Den Haag…)

Expected prior knowledge

Students should have some prior programming experience. It will be beneficial if you have dealt with a functional programming language like R or Python before. If you have never programmed before, you will have to be more active in lab sessions which will bring you up to speed.

Alignment courses: As a TPM Bachelor graduate, EPA 1333 Scientific Programming will help you in developing programming abilities, and the homework sessions from this class will allow you to practice your programming skills. As a non-TPM Bachelor student, you do not have to take EPA1333. In this course, you will learn how to program by following the lab/homework scripts and running them by yourself. We will provide ample opportunities in class and labs to discuss any issues/questions from the labs/homework.

Other faculties/universities: Graduate students from all faculties and exchange universities are welcome to join [subject to university/government restrictions around on-campus education]. There are similar courses in other faculties that are also more tailored to your respective programs, in case this course is not what you were expecting it to be.

For students who have had statistical, math or computer programming courses in their bachelors or elsewhere, this course will add to your skills by providing you with tools to become future policy-makers, data scientists, and in general, supporters of open science. The course will offer some uncertainty in terms of what is a problem and how it can be solved. If you are willing to embrace that uncertainty, we will learn about the fundamentals of urban data science. We may even discover new ways of designing equitable urban spaces, from neighbourhoods and cities to entire regions.

Philosophy of the course

  • (Lots of) methods and techniques
    • General overview
    • Intuition
    • Very little math
    • Lots of ways to continue on your own
  • Emphasis on application and use
  • Close connection to “real world” applications

Feedback strategy

The students will receive feedback through the following channels:

  • Formative Feedback weekly general feedback on labs by TAs and direct interaction with the instructor and teaching assistants in the lectures, labs and discussion sessions.
  • Summative Feedback as graded assessment of three summative assignments and a final project. This will be in the form of reasoning of the mark assigned as well as comments specifying how the mark could be improved. This will be provided before the submission of the next assignment is due so students have the chance to incorporate the feedback in their work.
  • Peer Feedback Online discussion forum maintained by the instructor where students can contribute by asking and answering questions related to the couse material.

Key texts and learning resources

Access to materials, including lecture slides and lab notebooks, is centralized through the use of a course website available through the following url:


Specific readings, videos, and/or podcasts, as well as academic references will be provided for each lecture and lab, and can be accessed through the course website.


This course has been developed using my research, input from colleagues at the faculty of Technology, Policy and Management at TU Delft and a few open-source teaching resources on the web. I am incredibly grateful to these developers for offering information openly:

  • Arribas-Bel, D. (2019). A course on geographic data science. Journal of Open Source Education, 2(16), 42.
  • Lab Materials extended from Introduction to Data Science taught at Harvard University by Pavlos Protopapas, Kevin A. Rader, and Chris Tanner.
  • All open-source material from Geoff Boeing at USC’s Sol Price School of Public Policy.


Unless otherwise stated, all content on this website, including all teaching material, is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.