Data Science

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

  • Rafael Irizarry, Biostatistics
  • Verena Kaynig-Fittkau, Computer Science

Staff

  • Stephanie Hicks

Lectures

Tu / Th

Labs