Description
: This course focuses on the application of data mining techniques and predictive analytics to business problem-solving. It covers key algorithms and techniques for extracting meaningful insights from business data, including data preprocessing, decision trees, neural networks, k-nearest neighbors, clustering, and association rules. Students will gain hands-on experience with data mining tools and software, applying these techniques in managerial contexts such as customer relationship management, marketing, sales, credit scoring, and churn analysis.
Posting limited to:
Professeur à temps-partiel régulier / Regular Part-Time Professor
Date Posted (YYYY/MM/DD):
2026/07/14
Applications must be received BEFORE (YYYY/MM/DD):
2026/08/15
Expected Enrolment:
40
Approval date:
2026/07/14
Number of credits:
3
Work Hours:
39
Hourly Rate:
Enseignement / Teaching: $239.47 (2024-2025)
The academic year starts on September 1 and ends on August 31.
These rates do not included vacation pay nor statutory pay.
These rates will be applied until a new collective agreement is ratified. Retro will be paid after the ratification.
Course type:
B
Posting type:
Régulier / Regular
Language of instruction:
Anglais | English
Competence in second language:
Active
Course Schedule:
Lundi | Monday 19:00-22:00 - -
Requirements:
- Education: Bachelor's degree in Business, Computer Science, Engineering, or related field is required; Master’s in Management or Engineering preferred. A Ph.D. is considered an asset.
- Industry Experience: Demonstrated track record in professional or managerial roles involving data analytics, data mining, or technology-driven decision-making. Experience as a CTO or equivalent leadership role in a data-intensive or tech-focused organization is highly desirable.
- Teaching Experience: Prior experience in post-secondary teaching or professional development instruction is preferred.
Technical and Analytical Skills
- Proficient in data mining and predictive analytics, with the ability to teach both supervised and unsupervised learning techniques, including decision trees, neural networks, k-nearest neighbors, clustering, and association rules; familiarity with tools such as RapidMiner, WEKA, and others is a plus.
- Extensive experience with IBM SPSS Modeler, including stream creation, model building and evaluation, and applying CRISP-DM within the visual interface.
- Ability to apply analytical techniques to managerial contexts such as CRM, marketing, sales, credit scoring, and churn analysis.
- Solid understanding of data preprocessing, including data cleaning, transformation, and partitioning.
Desirable Additional Skills
- Familiarity with tools such as RapidMiner, WEKA, and other data mining platforms.
- Knowledge of scripting or programming languages (e.g., Python, R, SQL)
- Experience with integrating SPSS Modeler with business systems or databases.
- Knowledge of modern data analytics trends and use of visual programming tools in business intelligence.
Additional Information and/or Comments:
An acceptable level of education and/or experience could be viewed as being equivalent to the educational required and/or demonstrated experience.