Course

Fundamentals of Machine Learning in Data Science

Faculty
Commerce & Business Administration
Department
Computing Studies & Information Systems
Course code
CSIS 3290
Credits
3.00
Semester length
15 Weeks
Max class size
35
Method(s) of instruction
Lecture
Seminar
Course designation
None
Industry designation
None
Typically offered
To be determined

Overview

Course description
In this course, students will learn to apply machine learning concepts to analyze data and make predictions. Students will learn how to collect and wrangle data, to explore data using statistics and visualizations, to transform data for further modeling, to model data using machine learning algorithms to predict data patterns, and to evaluate these model-based predictions. Students will be expected to have prior experience with fundamentals of programming.
Course content
  1. Programming language review for data analytics
    • Basic syntax, variables, control flow, loops, install and import libraries for data processing such as SciPy, NumPy, Pandas, Sci-Kit Learn, TensorFlow or other similar libraries and packages
  2. Data And Features: using libraries such as NumPy and Pandas                   
    • Represent data using lists, arrays for structured data
    • Work with data frames using packages such as Pandas to represent diverse data
    • Use Control Flow for filtering data and performing filtered computations
    • Manipulate Data using functions and packages to process the data and perform computations
    • Understand, determine and represent Features
    • Perform Data Wrangling
  3. Exploring Data: - using libraries such as Matplotlib                                     
    • Visualize Data by creating plots using tools such as Matplotlib
    • Perform high dimensionality visualizations
  4. Transforming Data: using libraries such as Sci-Kit learn                              
    • Create Data Transformers and apply dimensionality reducing techniques as PCA
  5. Data Modeling: using libraries such as Keras, TensorFlow and scikit-learn
    • Use machine learning techniques such as clustering, supervised learning, K-nearest neighbours, Regression to model the data
  6. Evaluating Data: Evaluate modeled data using evaluation techniques         
    • Create and apply confusion matrices
    • Perform cross-validation using scoring metrics
    • Implement and apply power tuning and pipelining to evaluate the data
Learning activities

Lecture, seminars, demonstrations, and hands-on exercises/projects

Means of assessment

The course evaluation is consistent with the Douglas College Evaluation Policy.

Labs 0 - 10%
Assignments  0 - 10%
Projects  15% - 30%
Midterm Exam* 30% - 40%
Final Exam* 30% - 40%
Total 100%
 

* Practical hands-on computer exam

In order to pass the course, students must, in addition to receiving an overall course grade of 50%, also achieve a grade of at least 50% on the combined weighted examination components (including quizzes, tests, exams).

Students may conduct research as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans, which can require obtaining Informed Consent from participants and getting the approval of the Douglas College Research Ethics Board prior to conducting the research.

Learning outcomes

At the end of this course, the successful student will be able to:

  • Use appropriate tools and libraries needed for data science.
  • Explain the concepts of data processing and feature engineering.
  • Conduct data wrangling for further machine learning processing.
  • Apply statistisc and visulization techniques for data exploration.
  • Employ suitable structures to transform data for modeling.
  • Utilize various machine learning models and algorithms for different applications.
  • Compare different models using appropriate evaluation metrics and strategies.
Textbook materials

Custom courseware, class notes provided by the instructor, and online resources or other textbooks as approved by the department

Requisites

Prerequisites

CSIS 1175 (minimum grade C)

Corequisites

No corequisite courses.

Equivalencies

 

 

Course Guidelines

Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester / year of the course, consider the previous version as the applicable version.

(Current)

Course Transfers to Other Institutions

Below are current transfer agreements from Douglas College to other institutions for the current course guidelines only. For a full list of transfer details and archived courses, please see the BC Transfer Guide.

Institution Transfer details for CSIS 3290
Athabasca University (AU) AU COMP 3XX (3)
College of New Caledonia (CNC) CNC CSC 2XX (3)
Kwantlen Polytechnic University (KPU) No credit
Simon Fraser University (SFU) No credit
Thompson Rivers University (TRU) TRU COMP 3XXX (3)
University Canada West (UCW) UCW CPSC 3XX (3)
University of Northern BC (UNBC) UNBC CPSC 3XX (3)
University of the Fraser Valley (UFV) UFV COMP 381 (3)

Course Offerings

Fall 2025

CRN Days Instructor Status
Maximum seats
35
Currently enrolled
35
Remaining seats:
0
On waitlist
13
Building
New Westminster - North Bldg.
Room
N5107
Times:
Start Time
9:30
-
End Time
12:20
Section notes

CSIS 3290 001-Students will NOT receive credit for both CSIS 3190 and CSIS 3290. Contact the Chair / Program Coordinator of the Department of Computing Studies and Information Systems if you have any questions.

CRN Days Instructor Status
Maximum seats
35
Currently enrolled
35
Remaining seats:
0
On waitlist
9
Building
New Westminster - North Bldg.
Room
N5107
Times:
Start Time
12:30
-
End Time
15:20
Section notes

CSIS 3290 002-Students will NOT receive credit for both CSIS 3190 and CSIS 3290. Contact the Chair / Program Coordinator of the Department of Computing Studies and Information Systems if you have any questions.