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APTPUO-Winter 2027-MEM5300 A (online)University of Ottawa • Ottawa, ON, CA
APTPUO-Winter 2027-MEM5300 A (online)

APTPUO-Winter 2027-MEM5300 A (online)

University of Ottawa • Ottawa, ON, CA
2 days ago
Job type
  • Part-time
Job description

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.

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APTPUO-Winter 2027-MEM5300 A (online) • Ottawa, ON, CA

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