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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 academic advisor should you notice any discrepancies.

The Honours BSc Data Science program 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. The program shall include no more than 7.0 100-level credits and must include the following:

BU111 - Understanding the Business Environment or ENTR100
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
MA120 - Foundations and Applications of Mathematics or MA121 - Introduction to Mathematical Proofs
MA122 - Introductory Linear Algebra or MA123 - Introductory Linear Algebra with Applications
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
One of: DATA200 - Data Analytics ,or BU425 - Business Analytics, or CP422 - Programming for Big Data

2.5 senior CP, MA, ST, DATA elective credits which must include at least 2.0 credits at the 300 or 400 level (See Program Regulation 7).


See our recommended schedule for taking courses.

CONCENTRATION REQUIREMENTS

Big Data Concentration

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

 
Financial Risk Analysis Concentration

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

MA170 - Introduction to Mathematics for Finance;
MA270 - Financial Mathematics I;
One of MA370 - Financial Mathematics II or MA373 - Introduction to Actuarial Mathematics;
One of MA477 - Quantitative Financial Risk Management or ST473 - Financial Data Analysis;
1.0 additional credit from ST courses at the 300 or 400 level (which must include ST359 if MA370 is taken)

MA371 shall be taken in the core.

 
Statistical Analysis Concentration

DATA200 - Data Analytics
0.5 credit from: ST358 and ST359
ST361 - Mathematical Statistics
0.5 credit from ST463 - Computational Statistics and ST474 - Monte Carlo Methods
0.5 additional credits from ST courses at the 300 or 400 level; MA371 shall be taken in the core.

Program Regulations

  1. Progression into Year 2 requires a minimum GPA of 5.00 in CP104 and CP164, successful completion of both MA103, successful completion of one of MA122 or MA123, and an overall GPA of 4.00. Progression into subsequent years requires a minimum overall GPA of 4.00. Graduation requires a minimum GPA of 5.00 in Mathematics (MA), Statistics (ST) and data (DATA) courses, a minimum GPA of 5.00 in Computer Science courses, and a minimum overall GPA of 5.0.
  2. Electives must include at least 0.5 credit from a discipline outside of those offered by the Faculty of Science.
  3. EC140 - Introduction to Macroeconomics is recommended as an elective.
  4. A Data Science BSc student cannot obtain a combined BSc major or minor in Mathematics, Financial Mathematics, Statistics, or Computer Science.
  5. A maximum of two Data Science Concentrations are permitted as part of the Honours BSc Data Science degree.
  6. 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 and MA103 would be completed in Winter term of Year 1.
  7. CP214 may not be used to fulfill senior CP credits in the Honours BSc Data Science program.
  8. Electives must not include UU201.