Course Description

Machine Learning is changing the way we live every day. Obtain the essential knowledge and skills on how machine learning and data science are applied in real life. Explore specific use cases in several data-driven industries. Learn how to apply machine learning to help businesses build better products, improve decision-making, and reduce costs.

This course is delivered in collaboration with WeCloudData

Course Details

Learning Outcomes

By the completion of this course, successful students will be able to:

  • Describe four practical use cases of data science including predictive churn modeling, customer segmentation, propensity modeling, and customer lifetime value
  • Describe three common data use cases in the Canadian job market including customer relationship management, risk management, and SaaS
  • Evaluate at least one industry use case using a machine learning algorithm  
  • Implement machine learning models using various techniques such as classification, regression, and unsupervised clustering algorithms and apply them in different real-world scenarios including credit scoring for risk management, acquisition models for sales, and churn modeling for marketing
  • Explain and present the machine learning outcome and actional insights in layman’s terms to business stakeholders with visual storytelling to support business decision making


  • Python for Statistics & Data Science
    • A refresher of Pandas Dataframe and basic data manipulation
    • Using python for statistical testing
    • Machine Learning Concepts and Algorithms (classification, regression, unsupervised clustering)
    • Writing your first ML algorithm from scratch
  • Use Case: AB Testing with Python for product growth 
  • Machine Learning Techniques (full lifecycle) 
    • A refresher of machine learning pipelines
    • Parameter tunings in Python
    • Dealing with an imbalanced dataset
    • Common feature engineering techniques
  • Industry Use Cases: 
    • Predictive Churn Modeling in Telecom
    • Customer Segmentation and Clustering in Retail
    • Time series forecasting with ML in Finance
    • Building retail credit risk models in Banking

Who is this course for?

This course is designed for:

  • Business associates, project managers
  • Professionals from every level or industry who work with analytics or data
  • Recent graduates and academics in Computer Science
  • Individuals who are aspired to pursue career opportunities like data scientist/data science analyst, predictive modeller, modeling analyst



Software Requirement:

To complete the labs activities and mini-project, you will require:

  • Anaconda Python Distribution


There are no mandatory prerequisites for this course, however you are required to perform self-assessment to ensure you meet the requirements to enrol.

Self-assessment for enrolment:

A minimum of 6 months experience in python programming

Recommended pre-requisites:

ICT 778 Python Foundations

Applies Towards the Following Program(s)

Enrol Now - Select a section to enrol in
Online Synchronous
5:00PM to 8:00PM
Nov 12, 2024 to Dec 03, 2024
Online Synchronous
8:00AM to 4:00PM
Nov 16, 2024 to Dec 07, 2024
Schedule and Location
Delivery Options
Course Fees
Flat Fee non-credit $1,495.00
Required Software
Zoom web conferencing software Laptop or computer installed with Windows or Mac OS Anaconda Python Distribution or Google Colab
Reading List / Textbook

No textbook required.

Section Notes

Classes are held online in real time (Mountain Time) at the specified time and dates.

This course uses:

  • D2L Learning Management System
  • Zoom web conferencing software.

This course is delivered in an online blended format, meaning that some classes are taught in a live virtual session using Zoom, and some work have to be completed in a designated online e-learning platform on your own time.

For the best experience, you will require access to a computer with Internet connection, a headset with speakers and microphone, webcam, and a monitor large enough to display multiple applications (or the use of two monitors). Your computer and internet connection should meet certain requirements. See the recommended requirements.

For more information, please visit our Online Learning Resources.

Students unfamiliar with online learning are encouraged to take our free Digital Skills for Learning Online course.

Unless otherwise stated, notice of withdrawal or transfer from a course must be received at least seven calendar days prior to the start date of the course.

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