CSCI 1109

Practical Data Science

Lec 01 and Lab B01 selected

This course will provide students with a practical overview of the world of data science research. Through hands-on use of Python’s data science libraries and publicly open datasets students will be exposed to the research methods and cutting-edge analyses used to effectively manipulate, process and investigate data from diverse disciplines.

Sections

Lecture
Lab

From the syllabus

From the Winter 2026 syllabus by Frank Rudzicz. Fall 2026 is taught by Marchand Y., so details may differ. Open the PDF

Grading

10%Participation and discussionTeam-based; in-class notebooks submitted after class score at most 33%
25%Weekly quizzesLowest quiz mark discarded
35%Practical assignments (5)Weighted 5%, 6%, 7%, 8%, and 9% by performance
15%Midterm exam
15%Final examCumulative; written and practical

Textbooks

  • Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
  • Probabilistic machine learning, Kevin Murphy

Course rules

Late notebooks
In-class notebooks may be submitted after class, but the maximum score is 33%.
AI tools
AI tools may support learning, but are prohibited on some assessments; acknowledge tools where allowed.
Absences
The Student Declaration of Absence policy applies.
Assignments
Assignments are completed individually and worked on during labs.
Remote proctoring
The instructor may require remote proctoring; contact them for an alternate assessment if unable to meet requirements.

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