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    • EES1137 Quantitative Applications for Data Analysis (Winter 2026)

      In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.  Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.

      There are three ways to follow this course:

      • As a graduate course for UofT PhD and MSc students. Students that wish to do so, should not self-enrol here, but enrol using ACORN/ROSI.
      • As a SciNet or Alliance user taking the course for SciNet certificate credits; for that, enrol on this site (note: limited spots).
      • As an auditor following along with recordings but not submitting assignments; for this no enrollment is needed.

      Teachers: Alexey Fedoseev, Erik Spence
      Date: Tue., 6 Jan. 2026 - 12:00 am
      Scientific Computing Credits: 8
      Data Science Credits: 28