The course is freely available on this website. There are no online or hybrid meetings. If you just want all of the course materials and not participate in the course any longer, please unenroll from Brightspace. Thank you.
Welcome to Introduction to Urban Data Science (EPA 1316 A) at Delft University of Technology. The course is taught by Dr Trivik Verma with the support of a fantastic team of teaching assistants.
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
A detailed schedule of the course is provided here.
All lectures, labs, discussions, and office hours will be hosted in person.
- Physical location information is mentioned in Brightspace calendars. All meetings will take place in The Hague campus of TU Delft.
- Virtual There are no online or hybrid meetings
- Announcements will regularly be made on Brightspace.
Teaching Assistant Support
|Auriane Técourt||A.C.Tecourt [at] student.tudelft.nl||All|
|Dorukhan Yeşilli||H.D.Yesilli [at] student.tudelft.nl||All|
|Ludovica Bindi||L.Bindi [at] student.tudelft.nl||All|
|Vaibhavi Srivastava||V.Srivastava [at] student.tudelft.nl||All|
|Nicolò Canal||N.Canal [at] student.tudelft.nl||All|
|Ruth Nelson||R.J.Nelson [at] tudelft.nl||All|
English & 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, I recommend doing a crash course in Python through Coursera or other online services before joining the class. This course is not about learning how to program.
Other faculties/universities: Graduate students from all faculties and exchange universities are welcome to join [subject to me receiving an email with your motivation to join the course before the start of the course. Without receiving such a motivation email, we will not be able to accommodate you fully.]. 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
- Very little math
- Lots of ways to continue on your own
- Emphasis on application and use
- Close connection to “real world” applications
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 Support Online discussion forum maintained by the instructor where students can contribute by asking and answering questions related to the couse material.
This course is about learning to learn, so if you have a question, follow the process laid out below for the most efficient and organised way of learning.
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.