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Artificial Intelligence for Business - Fall 25

DAT 105
Open Closing on September 15, 2025 / 3 spots left
Main contact
McMaster University Continuing Education
McMaster University Continuing Education
Hamilton, Ontario, Canada
Guido DiCesare
Instructor
(24)
6
Timeline
  • September 21, 2025
    Experience start
  • November 23, 2025
    Experience end
Experience
3 projects wanted
Dates set by experience
Preferred companies
Anywhere
Any company type
Any industries

Experience scope

Categories
Data visualization Data analysis Data modelling Data science
Skills
adult education applications of artificial intelligence business strategies complex problem solving artificial intelligence computer science data analysis
Learner goals and capabilities

This course is part of the Data Analytics certificate program. Students in the program

are adult learners with a post-secondary degree/diploma in computer science,

engineering, business, etc.


This course presents the principles of artificial intelligence (AI) through an exploration of

its history, capabilities, technologies, framework, and its future. AI applications in

various industries will be reviewed through some case examples. Current trends in AI

will be discussed and students will be encouraged to consider the potentials of AI to

solve complex problems. This course will help students to understand the implications

of AI for business strategy, as well as the economic and societal issues it raises

Learners

Learners
Certificate
Beginner, Intermediate levels
20 learners
Project
30 hours per learner
Learners self-assign
Teams of 4
Expected outcomes and deliverables

The final project deliverables will include:

  • A report on students’ findings and details of the problem presented
  • Future collaboration ideas will be identified based on current project outcomes
Project timeline
  • September 21, 2025
    Experience start
  • November 23, 2025
    Experience end

Project examples

The project(s) should provide an opportunity for the students to collaborate with the

project sponsor to identify and translate a real business problem into an AI analytics

problem. The projects can be short, where the students can apply their learnings to

address the sponsors business problem. Some examples are:


  • Describe AI capabilities and the AI technologies to your team
  • Discover how AI can be exploited through some case studies
  • Identify the risks of AI
  • Apply some of the AI tools such as TensorFlow and the services such as IBM Watson cognitive services
  • Build an AI agent or an AI application using AI services and AI tools


You should submit a high-level proposal/business problem statement including

relevant data sets and definitions, a list of acceptable tools (if applicable), and

expected deliverables. Business datasets could be provided based on a non-

disclosure agreement or in an anonymized/synthetic data format that is relevant to

your organization and business problem. The course instructors will review the

documents to confirm the scope and timing of the proposed problem and its

alignment with the capstone course requirements.


Analytics solution may be applicable for (however they are not limited to) the following

topics:


1. Customer acquisition and retention

2. Cross-sell and upsell opportunities

3. Develop high propensity target markets

4. Customer segmentation (behavioral or transactional)

5. New Product/Product line development

6. Ranking markets by potential revenue