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Data Analytics and Modelling - Fall 25
DAT 201
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 offers an introduction to data science and machine learning paving the way for students to learn data analytics principles. In particular, this course begins with a brief history of data analytics and data science, followed by regression analysis, regression and classification trees, and ends with introductions to K-means clustering, principal component analysis (PCA). Each lecture has associated with it a practical lab session in which students will put "theory into practice" offering students a hands-on approach to learning the material.

Artificial Intelligence for Business - Fall 25
DAT 105
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

Data Programming II - Fall 25
DAT 303
This course is part of the Big Data Programming and Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course is designed to present the fundamental concepts and theories in Data Analytics and promote the application to the workplace and professional practice. Students begin with an exploration of MongoDB which is a document database with scalability and flexibility for queries and indexing, and progress to the ELK stack – a technology stack used for logging with different components, such as Elasticsearch, Logstash, and Kibana. Course activities will include instructor presentations, required readings and experiential learning activities (i.e. case studies, group discussions, projects, etc.).

Machine Learning for Big Data Analytics - F25
DAT 301
This course is part of the Big Data Programming and Analytics certificate programs. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course builds on the fundamental principles of data analytics, this course advances to modern machine learning techniques such as neural network, deep learning, and reinforcement learning as well as NLP and text analysis. Application activities are structured to provide an introductory level of how machine learning techniques are applied to big data analytics.