This repository contains learning materials for CP4813 Urban Data Science at Georgia Institute of Technology, School of City and Regional Planning
Course name: CP4815 Urban Data Science Instructor: Yiyi He Teaching assistant: Yuehan Zhang
Contact email addresses:
- yiyi.he@design.gatech.edu (Instructor)
- yhzhang0725@gatech.edu (GTA)
In today’s world, understanding cities requires more than just traditional methods. Urban planners and social scientists are increasingly turning to data science techniques to gain deeper insights into the complex issues that cities face. This course serves as an introduction to data science for undergraduate and graduate students in urban planning and related fields.
Throughout this course, you will delve into the interdisciplinary field of data science, which combines scientific methods, algorithms, and systems to extract valuable insights from diverse datasets. We will explore how data from various sectors, such as transportation, housing, and the physical environment, can be analyzed to understand urban dynamics better.
You will develop a solid foundation in key data science concepts and techniques using the programming language Python. We will begin by covering essential topics such as data import, cleansing, and transformation, laying the groundwork for more advanced analyses. As we progress, we will introduce data visualization techniques tailored to the needs of urban planners, emphasizing effective communication of findings and insights.
By the end of this course, you will have acquired fundamental skills and tools essential for conducting data-driven analyses in urban planning. Whether you're an undergraduate embarking on your academic journey or a graduate student preparing for advanced research, this course will provide you with the necessary expertise to tackle real-world urban challenges. Moreover, the knowledge gained here will serve as a solid foundation for future coursework and research endeavors, empowering you to confidently apply data science techniques to your capstone, thesis, or dissertation work.
- Understand the fundamental concepts, theories, and models of urban data analytics.
- Collect, import, tidy, export, and manipulate data effectively and efficiently.
- Have the necessary quantitative, GIS, and Python programming skills for analyzing urban issues and problems through a series of hands-on lab exercises.
- Identify urban problems/research questions and solve them in a reproducible way using spatial analysis/visualization techniques through final projects.
- Engage in hands-on projects and case studies that apply data science techniques to real-world urban challenges, fostering problem-solving and critical thinking skills.
📖 Python for Data Analysis (3rd Edition)
Author: Wes McKinney
Link to book content: https://wesmckinney.com/book
Link to Github Repo: https://github.com/wesm/pydata-book/tree/3rd-edition
📖 Geographic Data Science with Python
Authors: Sergio J. Rey, Dani Arribas-Bel, and Levi J. Wolf
Link to book content: https://geographicdata.science/book/intro.html
📖 Python Data Science Handbook
Author: Jake VanderPlas
Link to book content: https://jakevdp.github.io/PythonDataScienceHandbook
Link to GitHub Repo: https://github.com/jakevdp/PythonDataScienceHandbook
📖 The Elements of Statistical Learning
Authors: Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Link to book content: https://www.sas.upenn.edu/~fdiebold/NoHesitations/BookAdvanced.pdf
GitHub and Git
Link to GitHub Documentation: https://docs.github.com/en/get-started/start-your-journey/about-github-and-git
Python Crash Course
Author: Srebalaji Thirumalai
Link to GitHub Repo: https://github.com/srebalaji/python-crash-course