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. All lectures will be posted here and should be available 24 hours after meeting time. You will also be able to find the Labs and Videos here as well.

The course is also listed as ac209a, stat121a, and E-109a.

Lectures and Sections

  • Lectures are 2:30-4pm on Mondays & Wednesdays in NW-B103 (Northwest Building)
  • Labs are HERE.
  • Section times can be dound on the schedule page


  • Pavlos Protopapas, IACS
  • Kevin Rader, Statistics
  • Rahul Dave, IACS
  • Margo Levine, Applied Math


  • Eleni Kaxiras, Head TF
  • And many others

Please be aware, that we will not publicly release the homework assignments this year. If you want to follow the course online without registering, you can use the assignments from 2013 and 2014, available at the links below. Additionally, the material from 2015 is also available.

Previous Material