Data Science Minor (B.Sc.) (24-25 credits)
Offered by: Mathematics and Statistics (Faculty of Science)
Degree: Bachelor of Science
Program credit weight: 24-25
Program Description
The B.Sc. Minor in Data Science complements science majors outside of Statistics and Computer Science. It offers essential core concepts and skills to engage in data science initiatives.
The B.Sc. Minor in Data Science can be completed with a minimum of 24 credits or a maximum of 25 credits.
Program note:
The B.Sc. Minor in Data Science cannot be combined with any of the following programs: the Major in Statistics, the Liberal program CSC Statistics, the Honours in Statistics, any program offered by the School of Computer Science and any joint program that has Computer Science as a component. As a precaution, students are strongly encouraged to discuss their eligibility with an adviser from the Department of Mathematics and Statistics before enrolling in the program. Students may use up to six credits of overlap between the Minor and their primary program.
Program Prerequisites (10 credits)
Students entering the BSc Minor in Data Science program from the Foundation program must have completed the courses below or their
equivalents. Quebec students must have completed the DEC with appropriate science and mathematics courses.
| Course | Title | Credits |
|---|---|---|
| MATH 133 | Linear Algebra and Geometry. | 3 |
Linear Algebra and Geometry. Terms offered: Summer 2026, Fall 2026, Winter 2027 Systems of linear equations, matrices, inverses, determinants; geometric vectors in three dimensions, dot product, cross product, lines and planes; introduction to vector spaces, linear dependence and independence, bases. Linear transformations. Eigenvalues and diagonalization. | ||
| MATH 140 | Calculus 1. | 3 |
Calculus 1. Terms offered: Summer 2026, Fall 2026, Winter 2027 Review of functions and graphs. Limits, continuity, derivative. Differentiation of elementary functions. Antidifferentiation. Applications. | ||
| MATH 141 | Calculus 2. | 4 |
Calculus 2. Terms offered: Summer 2026, Fall 2026, Winter 2027 The definite integral. Techniques of integration. Applications. Introduction to sequences and series. | ||
Required Courses (15 credits)
| Course | Title | Credits |
|---|---|---|
| COMP 250 | Introduction to Computer Science. | 3 |
Introduction to Computer Science. Terms offered: Fall 2026, Winter 2027 Object oriented programming in Java: classes and objects, interfaces, inheritance. Data structures and abstract data types: arrays, stacks, queues, linked lists, trees, binary trees,binary search trees, heaps, hash tables, graphs. Recursive and non-recursive algorithms and their asymptotic complexity: searching and sorting, tree and graph traversal. Mathematical tools: induction, recurrence relations. | ||
| COMP 260 | Applied Algorithms and DataStructures. | 3 |
Applied Algorithms and DataStructures. Terms offered: Fall 2026 Foundational algorithms and data structures with a focus on their implementation and use in data science and applied computer science. Graph, hashing, dynamic programming, parsing, heuristic, and streaming data algorithms. Algorithmic correctness and efficiency. Data storage and memory footprint management. Introduction to relational databases, Unix systems and high-performance computing. | ||
| COMP 370 | Introduction to Data Science. | 3 |
Introduction to Data Science. Terms offered: Fall 2026 Comprehensive introduction to the data science process. Orientation to the use and configuration of core data science toolkits, data collection and annotation fundamentals, principles of responsible data science, the use of quantitative tools in data science, and presentation of data science findings. | ||
| MATH 208 | Introduction to Statistical Computing. | 3 |
Introduction to Statistical Computing. Terms offered: Fall 2026 Basic data management. Data visualization. Exploratory data analysis and descriptive statistics. Writing functions. Simulation and parallel computing. Communication data and documenting code for reproducible research. | ||
| MATH 209 | Fundamentals of Statistical Modeling and Inference. | 3 |
Fundamentals of Statistical Modeling and Inference. Terms offered: Winter 2027 Introduction to statistical modelling, likelihood principle and maximum likelihood estimation, Bayesian principle and Bayesian estimation, with emphasis on their application in statistical analysis and data science. | ||
Complementary Courses (9-10 credits)
0-3 credits selected from:
| Course | Title | Credits |
|---|---|---|
| COMP 202 | Foundations of Programming. | 3 |
Foundations of Programming. Terms offered: Summer 2026, Fall 2026, Winter 2027 Introduction to computer programming in a high level language: variables, expressions, primitive types, methods, conditionals, loops. Introduction to algorithms, data structures (arrays, strings), modular software design, libraries, file input/output, debugging, exception handling. Selected topics. | ||
| COMP 204 | Computer Programming for Life Sciences. | 3 |
Computer Programming for Life Sciences. Terms offered: Fall 2026, Winter 2027 Computer Science (Sci): Computer programming in a high level language: variables, expressions, types, functions, conditionals, loops, objects and classes. Introduction to algorithms, modular software design, libraries, file input/output, debugging. Emphasis on applications in the life sciences. | ||
| COMP 208 | Computer Programming for Physical Sciences and Engineering . | 3 |
Computer Programming for Physical Sciences and Engineering . Terms offered: Fall 2026, Winter 2027 Programming and problem solving in a high level computer language: variables, expressions, types, functions, conditionals, loops, objects and classes. Introduction to algorithms such as searching and sorting. Modular software design, libraries, file input and output, debugging. Emphasis on applications in Physical Sciences and Engineering, such as root finding, numerical integration, diffusion, Monte Carlo methods. | ||
0-3 credits selected from:
| Course | Title | Credits |
|---|---|---|
| MATH 203 | Principles of Statistics 1. | 3 |
Principles of Statistics 1. Terms offered: Summer 2026, Fall 2026, Winter 2027 Examples of statistical data and the use of graphical means to summarize the data. Basic distributions arising in the natural and behavioural sciences. The logical meaning of a test of significance and a confidence interval. Tests of significance and confidence intervals in the one and two sample setting (means, variances and proportions). | ||
0-3 credits from:
| Course | Title | Credits |
|---|---|---|
| MATH 206 | Applied Calculus and Linear Algebra. 1 | 3 |
Applied Calculus and Linear Algebra. Terms offered: Fall 2026 Linear algebra in real coordinate space: eigenvalues and diagonalization, applications; orthogonality, Gram-Schmidt process, orthogonal projection; spectral theorem for symmetric matrices; singular value decomposition; positive definite matrices. Multivariable calculus: partial derivatives; linear and quadratic approximation; directional derivatives and gradient; classification of extreme values; constrained optimization. Examples and applications in data science. | ||
| MATH 223 | Linear Algebra. | 3 |
Linear Algebra. Terms offered: Fall 2026, Winter 2027 Review of matrix algebra, determinants and systems of linear equations. Vector spaces, linear operators and their matrix representations, orthogonality. Eigenvalues and eigenvectors, diagonalization of Hermitian matrices. Applications. | ||
- 1
Students who have completed MATH 222 in their primary program cannot take MATH 206 must replace it with MATH 223. Students who have completed both MATH 222 and MATH 223 in their main program cannot take MATH 206.
0-10 credits selected from:
| Course | Title | Credits |
|---|---|---|
| COMP 345 | From Natural Language to Data Science. | 3 |
From Natural Language to Data Science. Terms offered: Winter 2027 Introduction to language data science, including theoretical approaches and practical skills. Processing, searching, and querying text data; making sense of large corpora; modelling and interpreting psycholinguistic and historical language data; building models of sequences of words; computing similarity between languages; information retrieval and extraction; question answering; and ethics. | ||
| COMP 445 | Computational Linguistics. | 3 |
Computational Linguistics. Terms offered: Fall 2026 Introduction to foundational ideas in computational linguistics and natural language processing. Topics include formal language theory, probability theory, estimation and inference, and recursively defined models of language structure. Emphasis on both the mathematical foundations of the field as well as how to use these tools to understand human language. | ||
| COMP 551 | Applied Machine Learning. | 4 |
Applied Machine Learning. Terms offered: Fall 2026, Winter 2027 Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems. | ||
| MATH 308 | Fundamentals of Statistical Learning. | 3 |
Fundamentals of Statistical Learning. Terms offered: Winter 2027 Theory and application of various techniques for the exploration and analysis of multivariate data: principal component analysis, correspondence analysis, and other visualization and dimensionality reduction techniques; supervised and unsupervised learning; linear discriminant analysis, and clustering techniques. Data applications using appropriate software. | ||
| MATH 423 | Applied Regression. | 3 |
Applied Regression. Terms offered: Fall 2026 Multiple regression estimators and their properties. Hypothesis tests and confidence intervals. Analysis of variance. Prediction and prediction intervals. Model diagnostics. Model selection. Introduction to weighted least squares. Basic contingency table analysis. Introduction to logistic and Poisson regression. Applications to experimental and observational data. | ||