MUSA 550 will be held remotely during fall 2020. Information regarding lectures, office hours, assignments, and grading can be found below.

Class

  • Tuesday & Thursdays, 6:00 PM to 7:30 PM
  • Lectures will be via Zoom — calendar invites to lectures can be found on the course’s Canvas page
  • Lectures will be recorded and available on Canvas

Contact Info

  • Instructor: Nick Hand, nhand@design.upenn.edu
  • Teaching Assistant: Eugene Chong, echong91@upenn.edu

Office Hours

Office hours will use Zoom and will be by appointment — you should be able to sign up for 1 (or more) 15-minute time slot via the Canvas calendar. Zoom information for each session is available in the meeting details.

Nick:

7:30am-9am and 6:00pm-7:30pm on Tuesdays

Eugene:

10:30am - 12:30pm on Thursdays

Course Websites

We will use Piazza for questions related to lecture materials and assignments, while Canvas will be used for hosting recorded lectures and calendar invites for Zoom lectures. The course’s Github page will have repositories for each week’s lectures as well as assignments.

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 the Github repository.

Grading

The grading breakdown is as follows: 50% for homework; 40% for final project, 10% for participation. Your participation grade will be determined by your activity on Piazza — both asking, answering, and reading questions.

Of the seven homework assignment, you must complete five in total and three are required. 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. The course will require a working installation of Python on your local computer. See the Software Setup Guide for instructions on how to setup your computer for use in this course.