Big Data Computing and Systems

The course introduces distributed computational techniques, distributed algorithms and systems/programming support for large-scale processing of data.

15 points

COSC262

Computational Data Methods

This course extends multivariate data science techniques to topics such as classification, data fitting, regularization and regression. The focus of this course is on the methods which support many modern data processing applications. Students will be introduced to multivariate statistical techniques, linear algebra and calculus topics that are needed in data science.

15 points

MATH203 or DATA203 or EMTH211

MATH303

Legal, Regulatory, and Policy Considerations Around AI Technologies

This course provides students with a basic understanding of the concept of artificial intelligence (AI) and the existing spectrum of AI technologies. It has an easily understandable, lay-person-accessible format, requiring no prior mathematical or computer science knowledge. The course also gives an overview of the evolving AI legal, regulatory, and policy landscape. Upon successful completion of the course, students will possess the necessary technical understanding, research, analytical, problem solving, as well as collaboration and communication skills to tackle legal, regulatory, and policy issues related to the development and societal adoption of AI technologies independently or as member of an interdisciplinary team.

15 points

, (1) Any 60 points at 200-level from Schedule C and S to the Bachelor of Data Science; or (2) LAWS101.

DATA416

For LLB students: LAWS202-206. For BDataSc and other non-LLB students: N/A.

Data Science Capstone Project

This course will develop your ability to undertake research in data science. Your project will be motivated by a real data science problem, and you will design and complete a research project towards a solution. You will work in a group, with group supervision and, where appropriate, will meet with your data science industry contact. The course consists of regular lectures/tutorials and project group meetings, supported by web-based resources. You will present your findings to other students and stake holders, and prepare a written report. The emphasis is on working together to solve real-life data science problems using skills that are transferable to the workplace.

30 points

Subject to approval of the Head of Department.