Course Overview
This course provides a foundation to conduct predictive analytics through a focus on each stage of the data modelling life cycle. Practical lab and assignment work include data exploration and preparation, development of continuous variable and classifier predictive models with statistical and machine learning algorithms. Evaluation metrics and model validation techniques are emphasized to provide balanced views of model strengths and weaknesses.
Prerequisite(s)
- No prerequisites are required for this course.
Credits
3.0
- Not offered this term
- This course is not offered this term. Notify me to receive email notifications when the course opens for registration next term.
Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Analyze large amounts of unstructured data.
- Prepare data for modelling.
- Apply treatments to properly manage time series data.
- Identify optimal model variable combinations through data inspection and automation.
- Select and apply common statistical and machine learning model algorithms for continuous and classification predictions.
- Conduct principal component analysis (PCA) for dimension reduction with unsupervised data.
- Apply clustering for segment grouping and also for imputing.
- Evaluate models with suitable model evaluation metrics.
- Implement proper model validation routines and analysis.
- Safely develop appropriate new predictive modelling algorithms that are customized for the task at hand.
Effective as of Fall 2025
Related Programs
Data Science (COMP 9170) is offered as a part of the following programs:
- Indicates programs accepting international students.
- Indicates programs eligible for students to apply for Post-graduation Work Permit (PGWP).
School of Computing and Academic Studies
- Applied Computing
Master of Science Full-time
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