This course builds on CSTP 1108 covering applied topics in calculus, probability and statistics which form the foundation of data science and are crucial for understanding and applying Machine Learning (ML) algorithms. Students will learn how these mathematical tools are used to analyze and interpret data, model real-world problems, and support decision-making in the field of data science. Practical examples and exercises in Python will help students apply mathematical principles to real data science tasks, preparing them for the technical challenges of working in this rapidly evolving field.
Prerequisites
CSTP 1108, CSTP 1105.
Course type
This course is offered as part of a VCC program and cannot be taken individually.
International students: Eligibility is determined at the program level. Visit the program page for international student requirements and application information.
- Math for data science, AI and machine learning
- Calculus and derivatives
- Vectors and Matrices
- Probability
- Statistical analysis and modelling
- Statistical foundation of Machine Learning
- Random variables ands various probability distribution
Course schedules
Select your program to see the available course schedules.
| CRN# | Duration | Delivery | Location | ||
|---|---|---|---|---|---|
| 71907 |
September 8, 2026 to November 12, 2026 |
Lecture | VCC Downtown Campus | See full schedule |
Online courses listed without scheduled meeting times can be completed on your own schedule. All self-paced courses have on-campus tests. Off-site certified invigilation centres may be used at the student's expense.
How to take this course
This course is offered as part of a VCC program only.
Contact us
If you have any questions, please email at [email protected].
† This information is intended as a guideline only. Program and course details are subject to change with the approval of VCC's Board of Governors.