APTPUO-summer 2026-MIA5126 Z

Description

: This course provides an introduction to the fundamental concepts, principles and methodologies of Data Science. Topics covered will include various concepts spanning the entire data science process, encompassing business understanding, exploratory data analysis, data preprocessing, data warehousing, and predictive and descriptive modeling. Through a blend of theoretical concepts and practical applications, students are expected to develop the hands-on skills and the knowledge necessary to effectively address real-world data science challenges across a range of industries upon completion of the course.

Posting limited to :

Professeur à temps-partiel régulier / Regular Part-Time Professor

Date Posted (YYYY / MM / DD) :

2026 / 01 / 26

Applications must be received BEFORE (YYYY / MM / DD) :

2026 / 02 / 27

Expected Enrolment : 40

Approval date : 2026 / 01 / 26

Number of credits : 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 : Posting type :

Régulier / Regular

Language of instruction :

Anglais | English

Competence in second language :

Active

Course Schedule :

Lundi | Monday 19 : 00-22 : 00 - -

Requirements :

• Ph.D. in DTI, Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Demonstrated expertise in Data Science, with a focus on areas such as statistical modeling, machine learning, data visualization, data warehousing, and big data analytics.
• Teaching experience at the graduate and / or undergraduate level, preferably in Data Science or related fields.
• Hands-on experience with industry tools and technologies commonly used in Data Science, such as Python, SQL, TensorFlow, etc.

Preference will be given to candidates with experience in project-based learning or experiential learning approaches in Data Science.

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.