Location:

Main Campus

Session:

2026 Winter Semester | Trimestre d'hiver

Faculty:

Faculté de génie / Faculty of Engineering

Unit:

School of Engineering Design and Teaching Innovation_PT

Course Title:

PRINCIPLES OF DATA ANALYTICS (On line)

Course Code:

MEM5300

Section:

B

Course 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:

August 08, 2025

Applications must be received BEFORE:

September 09, 2025

Expected Enrolment:

40

Approval date:

August 08, 2025

Number of credits:

3

Work Hours:

39

Course type:

B

Posting type:

Régulier / Regular

Language of instruction:

Anglais | English

Competence in second language:

Active

Course Schedule:

Mardi | Tuesday 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. If you are invited to continue the selection process, please notify us of any adaptive measures you might require. Information you send us will be handled respectfully and in complete confidence. Employees are required under provincial law to successfully complete all mandatory legislated training. The list of training may be modified by provincial law.

The hiring process will be governed by the current APTPUO collective agreements; you can click here for the main unit, here for the OLBI unit, or here for the Toronto/Windsor unit to find out more.

The University of Ottawa embraces diversity and inclusion in the workplace. We are passionate about our people and committed to employment equity. We foster a culture of respect, teamwork and inclusion, where collaboration, innovation, and creativity fuel our quest for research and teaching excellence. While all qualified persons are invited to apply, we welcome applications from qualified Indigenous persons, racialized persons, persons with disabilities, women and LGBTQIA2S+ persons. The University is committed to creating and maintaining an accessible, barrier-free work environment. The University is also committed to working with applicants with disabilities requesting accommodation during the recruitment, assessment and selection processes. Applicants with disabilities may contact [email protected] to communicate the accommodation need. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.

Prior to May 1, 2022, the University required all students, faculty, staff, and visitors (including contractors) to be fully vaccinated against Covid-19 as defined in Policy 129 – Covid-19 Vaccination. This policy was suspended effective May 1, 2022 but may be reinstated at any point in the future depending on public health guidelines and the recommendations of experts.