#UPenn: MUSA 620 - Data Wrangling and Data Visualization
SCHEDULING
Class: Wednesdays from 9am to 12pm in the Levin Building, room 111.
Office hours: Mondays 6pm-8pm and Tuesdays 1pm-3pm. Email galkamaxd at gmail to schedule a time.
OBJECTIVE
The purpose of this course is to familiarize students with the “pipeline” approach to data science. This involves the process of gathering data, storing the data, analyzing the data, and visualizing the data such that non-technical decision makers can make sense of it. The course is broken down accordingly into four sections.
- Data collection: Students will learn how to gather data by way of web scraping, APIs, and other unstructured sources.
- Databases: This part of the course teaches students how to store this data for efficient retrieval and analysis.
- Analytics: Students will learn a range of machine-driven techniques for analyzing structured and unstructured data.
- Data visualization: The last part of the course teaches students how to present the results of their analysis visually using R and the web application framework Shiny.
FORMAT
The course will be conducted in weekly sessions devoted to lectures, demonstrations and discussions.
ASSIGNMENTS
There is one required final project at the end of the semester. Homework will be assigned before the close of each class and will be due at the end of the following week’s class. Four of the homework assignments will be explicitly required. The remainder are optional, but will count toward the participation component of your final grade.
For the final project, students will replicate the pipeline approach on a dataset (or datasets) of their choosing. The final deliverable will be a web-based data visualization and accompanying description including a summary of the results and the methods used in each step of the process (collection, storage, analysis and visualization).
GRADING
The grading breakdown is as follows: 50% for homework; 40% for final project, 10% for participation
SOFTWARE
This course relies on use of the R Statistical Package in conjunction with Shiny and other associated extensions.
SCHEDULE
| Class # | Date | Topic | Notes |
|---|---|---|---|
| Week 1 | Jan 18 | Introduction / Data visualization concepts | Slides |
| Week 2 | Jan 25 | Working with Census data | Slides |
| Week 3 | Feb 1 | Web scraping with R | Slides |
| Week 4 | Feb 8 | Unstructured data: Twitter API | Slides |
| Week 5 | Feb 15 | Large datasets: NYC Taxi trip data with Google BigQuery | Slides |
| Week 6 | Feb 22 | Spatial databases: PostGIS | Slides |
| Week 7 | Mar 1 | Data frames and data manipulation with R: dplyr | Slides |
| Spring Break | |||
| Week 8 | Mar 15 | Natural language processing | Slides |
| Week 9 | Mar 22 | Data visualization with R: ggplot2 | Slides |
| Week 10 | Mar 29 | Interactive maps with R Leaflet | Slides |
| Week 11 | Apr 5 | Shiny 1 | |
| Week 12 | Apr 12 | Shiny 2 | |
| Week 13 | Apr 19 | Shiny 3 | |
| Week 14 | Apr 26 | In-class work on final projects |