Data Science is the study of methods to obtain insight from available data in order to understand, predict, and improve business strategy, products and services, marketing campaigns, medicine, public health and safety, and many other pursuits. Such methods involve elements of both Statistics and Computer Science, with a focus on three foundational components: (i) Database Management, (ii) Statistics and Machine Learning, and (iii) Distributed and Parallel Systems.

The Data Science plan is guided by a joint curriculum committee. This committee is chaired by a Director of Data Science, normally a faculty member chosen from either academic unit with the agreement of both. Along with the Director, the committee includes four faculty representatives, two appointed by each unit. In addition, the Associate Chair of Undergraduate Studies for Statistics and Actuarial Science and the Director of Undergraduate Studies for Computer Science serve ex officio on the committee. Curriculum changes introduced by the committee must receive approval from both units before being approved at the Faculty level. In addition to chairing the curriculum committee, the Director has responsibility for promoting the plan, both internally and externally, and for overall coordination.

The Faculty of Mathematics offers two Honours plans in Data Science, a BMath (Data Science) and a BCS (Data Science). The Data Science plans are offered jointly by the Department of Statistics and Actuarial Science and by the David R. Cheriton School of Computer Science. Students in the two plans graduate with a background in both Computer Science and Statistics, taking a combination of required and elective courses that together provide a solid foundation in this emerging area.

Students in this plan must satisfy all requirements for Honours Statistics and must satisfy the following additional constraints on course selection:

One of

MATH 239 Introduction to Combinatorics

MATH 249 Introduction to Combinatorics (Advanced Level)

One of

CS 136 Elementary Algorithm Design and Data Abstraction

CS 146 Elementary Algorithm Design and Data Abstraction (Advanced Level)

All of

CS 240 Data Structures and Data Management

CS 241 Foundations of Sequential Programs

CS 245 Logic and Computation

CS 246 Object-Oriented Software Development

CS 251 Computer Organization and Design

CS 341 Algorithms

CS 348 Introduction to Database Management

CS 350 Operating Systems

CS 451 Data-Intensive Distributed Computing

STAT 341 Computational Statistics and Data Analysis

One of

CS 485 Machine Learning: Statistical and Computational Foundations

CS 486 Introduction to Artificial Intelligence

STAT 441 Statistical Learning - Classification

Two additional courses from the following list

CS 485 Machine Learning: Statistical and Computational Foundations

CS 486 Introduction to Artificial Intelligence

STAT 431 Generalized Linear Models and their Applications

STAT 440 Computational Inference

STAT 441 Statistical Learning - Classification

STAT 442 Data Visualization

STAT 443 Forecasting

STAT 444 Statistical Learning - Function Estimation