CMSC 207: Data Science and Visualization
Spring 2014, Prof. Sorelle Friedler
An introduction to techniques for the automated and human-assisted analysis of data sets. These “big data” techniques are applied to data sets from multiple disciplines and include cluster, network, and other analytical methods paired with appropriate visualizations.
Project Details (pdf)
Class Google Drive Directory for slides (must be signed into your haverford.edu account - Bryn Mawr students should request access to their personal gmail account).
The course will use selections from the following textbooks (all available for free online):
- Mining of Massive Datasets by Anand Rajaraman and Jeffrey D. Ullman.
- Visualization Design and Analysis: Abstractions, Principles, and Methods by Tamara Munzner. Still in draft form.
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
- Interactive Data Visualization for the Web by Scott Murray.
Scheduling details (such as course time, office hours, lab deadlines, etc.) are listed in the syllabus and, more conveniently, can be found on the course Google Calendar. The Google Calendar will be kept up-to-date in the event of any changes to the schedule.
Course Google Calendar
You may also be interested in the Computer Science Department Events Google Calendar.
Labs will be posted here as they are available. You should start working on the next lab listed as soon as the deadline for the previous lab has passed. Any work done before then (based on a potentially tentative lab description) is at your own risk! See the course google calendar for the most up-to-date deadlines.
- Lab 0 - HTML / CSS basics needed for future labs as well as d3 setup
- Lab 1 - linear regression analysis and visualization
- Lab 2 - hierarchical clustering, nearest neighbors, and visualizations
- Lab 3 - k-means clustering or correlation clustering and visualizations
- Lab 4 - network analysis (PageRank) and visualizations
If you haven't worked in the Haverford CS lab before, you may find the following links useful when setting yourself up in the lab.