# 👋🏽 Introduction ```{admonition} IMPORTANT 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 Spatial Data Science (EPA 122A) at Delft University of Technology. The course is taught by Dr Trivik Verma with the support of a fantastic team of teaching assistants. ## Instructor Details ```markdown Dr Trivik Verma Associate Professor in Urban Science & Policy B2.390, Building 31 Faculty of Technology, Policy and Management Jaffalaan 5 2628 BX Delft The Netherlands Email: t.verma@tudelft.nl Office Hours: Mondays 9-11 am (walk-in, no appointment needed) ``` ## Schedule A detailed schedule of the course is provided [here](introduction/schedule.md). ## Locations 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 | Teaching Assistant | Email | Role | |-------------------- |---------------------------------------- |----------------- | | Laura van Geene | L.A.vanGeene@student.tudelft.nl | All | | Nachiket Kondhalkar | N.B.Kondhalkar@student.tudelft.nl | All | | Philip Mueller | P.Muller@student.tudelft.nl | All | Shreya Kejriwal | S.M.Kejriwal@student.tudelft.nl | All | ## 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, 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. It is about becoming responsible data scientists. _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 spatial 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 **critical thinking**, **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 and labs. * **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. ## Questions 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. ![mindmap](introduction/mindmap.png) ## 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: [`https://trivikverma.github.io/spatial-data-science/_index.html`](https://trivikverma.github.io/spatial-data-science/_index.html) 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. ## Acknowledgement This course has been developed using 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](https://jose.theoj.org/papers/10.21105/jose.00042). 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](https://geoffboeing.com/) at USC’s Sol Price School of Public Policy. * Discussions with Francisco Rowe, Caitlin Robinson, Clara Peiret-Garcia, Juliana Goncalves, Anastassia Vybornova, Ruth Nelson, Nazli Aydin and so many students. ## License Unless otherwise stated, all content on this website, including all teaching material, is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).