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 temps-partiel rgulier / 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 current collective agreement is ratified.

Retro will be paid after the ratification.

Course type: B Posting type: Rgulier / 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; Masters 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 Rapid

Miner, 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 Rapid

Miner, 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.

📌 APTPUO-Winter 2027-A (online) (Ottawa)
🏢 University of Ottawa
📍 Ottawa