vcc.ca
CSTP 2108: Mathematics for Programmers
Effective date
September 2025
Department
Computer Systems Tech Diploma
School
Trades, Technology and Design
Description
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.
Credits
2.0
Year of study
2nd Year Post-secondary
Prerequisites
CSTP 1108, CSTP 1105.
Corequisites
None
Course Learning Outcomes
Upon successful completion of this course, students will be able to:
- Demonstrate core proficiency in working with vectors and multi-dimensional data in the context of Software Development and Data Science.
- Demonstrate the ability to perform basic calculus operation such as differentiation and integration to solve problems relevant to data science and machine learning.
- Describe the basic concepts in applied probability and statistics.
- Use relevant concepts in applied probability and statistics to analyze data, identify patterns, and make informed decisions based on data analysis.
- Extract machine-learning relevant information from a statistical distribution.
- Interpret the results of data analysis and effectively communicate their findings to support decision-making processes.
- Apply mathematical concepts, such as algorithms and computational complexity, to analyze software engineering problems.
Prior Learning Assessment & Recognition (PLAR)
None
Hours
Lecture, Online, Seminar, Tutorial: 30
Clinical, Lab, Rehearsal, Shop, Kitchen, Simulation, Studio: 10
Total Hours: 40
Clinical, Lab, Rehearsal, Shop, Kitchen, Simulation, Studio: 10
Total Hours: 40
Instructional Strategies
Instructional strategies include classroom lectures, demonstrations, group discussions, computer lab and hands-on practical work.
Grading System
Letter Grade (A-F)
Passing grade
C
Evaluation Plan
|
Type |
Percentage |
Assessment activity |
|
Assignments |
60 |
5 to 7 assignments |
|
Midterm Exam |
20 |
|
|
Final Exam |
20 |
|
Course topics
- 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
Notes:
- Course contents and descriptions, offerings and schedules are subject to change without notice.
- Students are required to follow all College policies including ones that govern their educational experience at VCC. Policies are available on the VCC website at:
https://www.vcc.ca/about/governance--policies/policies/. - To find out if there are existing transfer agreements for this course, visit the BC Transfer Guide at https://www.bctransferguide.ca.