CS109 Data Science

Hubway Clustering

Predicting Hubway Stations Status by Lauren Alexander, Gabriel Goulet-Langlois, Joshua Wolff

Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.

We will be using Python for all programming assignments and projects.

The course is also listed as AC209, STAT121, and E-109.

Instructors

  • Pavlos Protopapas, SEAS
  • Kevin Rader, Statistics
  • Mark Glickman, Statistics
  • Chris Tanner, SEAS
  • Joe Blitzstein, Statistics
  • Hanspeter Pfister, Computer Science
  • Verena Kaynig-Fittkau, Computer Science

Material from CS 109 taught from present to 2013

  • 2020: Protopapas, Rader, Tanner and Glickman
  • 2019: Protopapas, Rader, Tanner and Glickman
  • 2018 Protopapas, Rader, Tanner and Glickman
  • 2017 Protopapas, Rader
  • 2016
  • 2015 Blitzstein, Pfister, Kaynig-Fittkau
  • 2014
  • 2013 Blitzstein, Pfister