


- Description
-
McMaster University Continuing Education (MCE) has inspired people to discover and achieve through lifelong learning since 1931. As one of Canada’s largest and leading providers of academic certificate and diploma programs, professional development education, and corporate training, MCE’s purpose is to develop and realize the potential of both individuals and society by providing the education to thrive in a dynamic world. Our team is approachable, professional, and passionate. We offer more than 200 courses and workshops that blend both theoretical knowledge and practical application to help you build your skills and expand your career. Areas of focus include Technology, Business, Accounting, Marketing, Health, Social Science, Mindfulness, and Human Resources.
Recent experiences
Data Analytics and Modelling - Spring 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.
Data Analytics and Modelling - Spring 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.
Data Management - Spring 2025
DAT 202
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 explores the importance of managing data as an enterprise asset and the processes and components required in terms of the acquisition, storage, sharing, validation and accessibility of data for addressing business problems. An examination of Database Management Systems, database architectures (structured and non-structured) the differences between OLTP (Online transaction processing) OLAP (online analytical processing) as well as the administrative processes (Data Governance) that guide the data lifecycle will be a focus.
Data Programming II - Spring 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.).
Latest feedback

Experience feedback


Experience feedback



Experience feedback

