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Program Requirements

Note: University regulations apply to all students at Laurier. If there is any discrepancy between the program or progression requirements outlined on this page and those in the university's academic calendars, the academic calendars are the official sources of information. The information below is from the latest calendar, and you may be following progression requirements from an earlier calendar. Students are responsible for checking the appropriate calendar. Contact your program coordinator should you notice any discrepancies.

The Honours Bachelor of Science (BSc) in Data Science consists of a minimum of 20.0 credits.

The program follows a hub-and-spoke structure. The core (“hub”) of the program consists of 12.0 credits. The concentration (“spoke”) consists of 2.5 credits. The remaining 5.5 credits are elective credits, which may include additional courses in Business, Economics and Communication Studies.

The program shall include no more than 7.0 100-level credits and must include the following:

  • BU111: Understanding the Business Environment or ENTR100: Introduction to Business Principles for Entrepreneurs
  • CP104: Introduction to Programming
  • CP164: Data Structures I
  • CP213: Introduction to Object-Oriented Programming
  • CP264: Data Structures II
  • CP312: Algorithm Design and Analysis I
  • CP317: Software Engineering
  • CP363: Database I
  • CP373: Ethics and Professional Practice in Computer Science
  • CP321: Data Visualisation
  • CP322: Machine Learning
  • CP421: Data Mining
  • DATA100: Introduction to Data Analytics
  • MA103: Calculus I
  • MA121: Introduction to Mathematical Proofs or MA120: Introduction to Discrete Structures
  • MA122: Introductory Linear Algebra
  • MA200: Advanced Calculus
  • MA238: Discrete Mathematics
  • MA371: Computational Methods for Data Analysis or ST361: Mathematical Statistics
  • ST259: Probability I
  • ST260: Introduction to Statistics
  • ST362: Regression Analysis
  • ST494: Statistical Learning and Data Analysis
  • 0.5 senior BU credit (BU425: Business Analytics is recommended)
  • 2.5 senior CP, MA, ST, DATA elective credits, which must include at least 2.0 senior credits of which at least 1.5 credits must be at the 300 or 400 level.

See our recommended schedule for taking courses.

Concentration Requirements

You may complete one or two of three optional concentrations:

  • Concentration in Financial Risk Analysis
  • Concentration in Big Data
  • Statistical Analysis

The concentration(s), if declared, will appear on your transcript.

Note: The credits required for a concentration count toward the CP/DATA/MA/ST electives.

The following courses are required for the Concentration in Financial Risk Analysis:

  • MA170: Introduction to Mathematics for Finance
  • MA270: Financial Mathematics I
  • MA477: Quantitative Financial Risk Management
  • ST473: Financial Data Analysis
  • One course from: MA348, MA492, ST361, ST474
  • MA371 shall be taken in the core program requirements

The following courses are required for the Concentration in Big Data:

  • CP372: Computer Networks
  • CP476: Internet Computing
  • CP468: Artificial Intelligence
  • CP422: Programming for Big Data
  • CP423: Text Retrieval and Search Engines

The following courses are required for the Concentration in Statistical Analysis Concentration:

  • DATA200: Data Analytics
  • 0.5 credit: ST358 and ST359
  • ST361-Mathematical Statistics
  • ST463: Computational Statistics
  • 0.5 Additional credit from ST courses at the 300 or 400 level; MA371 taken in the core

Program Regulations

  1. For progression and graduation, you must meet the following conditions: minimum cumulative GPA of 5.00 in MA, ST, DATA credits; minimum cumulative GPA of 5.00 in CP credits; and an overall GPA of 5.00. You must meet the progression conditions in each year of the program by Aug. 31.
  2. Based on the results of the Calculus Preparation Evaluation, an entering student may be advised to complete MA102 prior to completing MA103; then MA102 would be completed in fall term of year 1, MA103 would be completed in winter term of year 1.
  3. Electives must include at least 0.5 credit from a discipline outside of those offered by the Faculty of Science.
  4. A Data Science BSc student cannot obtain a minor in Mathematics, Statistics or Computer Science.
  5. A maximum of two Data Science concentrations are permitted as part of the Honours BSc in Data Science degree.