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MUSA 620 - Geospatial Data Science in Python
University of Pennsylvania, Stuart Weitzman School of Design

Scheduling

Class

Thursday from 5pm to 8pm in Meyerson Hall, room B4.

Contact Info

Office hours

Nick:

  • 6-8pm, Mondays
  • Remotely via Google Hangouts ([email protected]).
  • Easiest by appointment, so please send me an email if you'd like to chat.

Chloe:

  • Tuesdays, 2-4pm
  • On campus in EC 226

Course Websites

Course Description

This course will provide students with the knowledge and tools to turn data into meaningful insights, with a focus on real-world case studies in the urban planning and public policy realm. Focusing on the latest Python software tools, the course will outline the “pipeline” approach to data science. It will teach students the tools to gather, visualize, and analyze datasets, providing the skills to effectively explore large datasets and transform results into understandable and compelling narratives. The course is organized into five main sections:

  1. Exploratory Data Science: Students will be introduced to the main tools needed to get started analyzing and visualizing data using Python.
  2. Introduction to Geospatial Data Science: Building on the previous set of tools, this module will teach students how to work with geospatial datasets using a range of modern Python toolkits.
  3. Data Ingestion & Big Data: Students will learn how to collect new data through web scraping and APIs, as well as how to work effectively with the large datasets often encountered in real-world applications.
  4. Geospatial Data Science in the Wild: Armed with the necessary data science tools, students will be introduced to a range of advanced analytic and machine learning techniques using a number of innovative examples from modern researchers.
  5. From Exploration to Storytelling: The final module will teach students to present their analysis results using web-based formats to transform their insights into interactive stories.

Format

The course will be conducted in weekly sessions devoted to lectures, interactive demonstrations, and in-class labs.

Assignments

There is one required final project at the end of the semester, and you must complete five of the seven homework assignments. Four of the assignments are required, and you are allowed to choose the last assignment to complete (out of the remaining three options). The required assignments are denoted by asterisks below.

For the final project, students will replicate the pipeline approach on a dataset (or datasets) of their choosing. Students will be required to use several of the analysis techniques taught in the class and produce a web-based data visualization that effectively communicates the empirical results to a non-technical audience. The final product should also include a description of the methods used in each step of the data science process (collection, analysis, and visualization).

For more details on the final project, see its repository.

Grading

The grading breakdown is as follows: 50% for homework; 40% for final project, 10% for participation. Your participation grade is a function of both in-class participation and Piazza participation.

Of the seven homework assignment, you must complete five, Three are required (denoted by the asterisk below). Late homework will be accepted but penalized.

Software

This course relies on use of Python and various related packages and for geospatial topics. All software is open-source and freely available.

Academic Integrity

Students are expected to be familiar with and comply with Penn’s Code of Academic Integrity, which is available in the Pennbook, or online at https://catalog.upenn.edu/pennbook/code-of-academic-integrity.

Schedule

Class # Date Topic Homework
Week 1 Aug 29 Exploratory Data Science in Python Assign HW #1 *
Week 2 Sep 5 Data Visualization Fundamentals Assign HW #2 *
Week 3 Sep 12 Geospatial Data Analysis and GeoPandas
Week 4 Sep 19 More Interactive Data Viz, Working with Raster Datasets Assign HW #3 *
Week 5 Sep 26 Getting Data Part 1: Working with APIs
Week 6 Oct 3 Getting Data Part 2: Web Scraping Assign HW #4
Fall Break Oct 10
Week 7 Oct 17 Analyzing and Visualizing Large Datasets Assign HW #5
Week 8 Oct 24 Case Study: Advanced Raster Analysis
Week 9 Oct 31 Case Study: OpenStreetMap, Urban Networks, and Interactive Web Maps Assign HW #6
Week 10 Nov 7 Case Study: Clustering Analysis in Python
Week 11 Nov 14 Predictive Modeling Part 1: Home Prices in Philadelphia Assign HW #7 *
Week 12 Nov 21 Predictive Modeling Part 2: Space/time Rideshare Demand
Week 13 Nov 26 From Notebooks to the Web: Github Pages, Web Servers, and Dash Proposal for final project due
Thanksgiving Break Nov 28
Week 14 Dec 5 From Notebooks to the Web: Dashboarding with Panel and the PyViz Ecosystem

* Denotes a required homework assignment

Assignment dates of homework are tentative and subject to change