EPA 1316 Introduction to Urban Data Science

Welcome to EPA 1316 at Delft University of Technology. The course used to be called Data Analytics and Visualisation and has now been named Introduction to Urban Data Science with a major overhaul. The entire course will be held online.


Dr Trivik Verma - t.verma [at] tudelft.nl


Assistant Professor in Computational Urban Science & Policy
B1.290, Building 31
Faculty of Technology, Policy and Management
Jaffalaan 5
2628 BX Delft
The Netherlands

The schedule for the course is:

  • Lectures: Wed and Fri 0915-1030 and 0830-0945 respectively, Online
  • Computer Labs: Wednesdays 1045-1245, Online
  • Paper Discussions: Fridays 1000-1100, Online

Course hosted Online @: Virtual Classroom on BigBlueButton


In 2020, the university has decided to host all lectures online so students can have a safe experience while still being able to continue their studies. All lectures, labs, discussions, office hours and questions will be hosted online (see below for more information). You will all receive a link for the course where everyone can interact virtually. I want to see you all in person, but desperate times require us all to cooperate. I am thankful that you are all here.

Teaching Assistants

Teaching AssistantEmailRole
Aarthi SundaramA.MeenakshiSundaram [at] student.tudelft.nlAll
Jelle EgbersJ.T.Egbers [at] student.tudelft.nlAll
Tess KimN.Kim-2 [at] student.tudelft.nlAll
Lotte LourensL.Lourens [at] student.tudelft.nlAll
Amir Ebrahimi FardA.EbrahimiFard-1 [at] tudelft.nlAll
Giulia ReggianiG.Reggiani [at] tudelft.nlDiscussion only
Bramka JafinoB.A.Jafino [at] tudelft.nlDiscussion only
Talia KaufmannVisiting PhD (no emails)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 are not required to have prior programming experience. However, 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 homework sessions that will bring you up to speed.

Alignment courses: If you are taking EPA 1333, it will help you in developing programming abilities, and the homework sessions from this class will allow you to practice your programming skills. If you are not taking EPA 1333, then you will learn how to program by following the homework scripts and running them by yourself. We will provide ample opportunities in class and labs to discuss any issues/questions from the homework. Graduate students from all faculties are welcome to join. Due to limited resources, especially in online formats, the class will be restricted to a certain number of students and students at the faculty of Technology, Policy and Management will be given first preferences. There are similar courses in other faculties that are also more tailored to your respective programs, in case this course is full.

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, consultants, 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 it, 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 the application and use
  • Close connection to “real world” applications

Feedback strategy

The students will receive feedback through the following channels:

  • Formal assessment of four summative assignments. This will be on the form of reasoning of the mark assigned as well as comments specifying how the mark could be improved. This will be provided no later than two working weeks after the deadline of the assignment submission.
  • Direct interaction with the instructor and teaching assistants in the lectures, labs and discussion sessions.
  • Online discussion forum maintained by the instructor where students can contribute by asking and answering questions related to the module.

Key texts and learning resources

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


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