Fall 2023 and Winter 2024 Application Portal Information
We are extending our admission deadline to provide further access and support to our applicants. The Fall 2023 deadline will now be July 3rd, 2023, 11:59 MST for Canadian/Permanent Residents. The application portal for Fall 2023 is now closed for international applications.
The application portal for Winter 2024 is now open. The Winter 2024 application deadline is September 1st, 2023, 11:59 MST for International applicants and October 3rd, 2023, 11:59 MST for Canadian/Permanent Residents.
Once students have completed the certificate's four courses (DATA 601, 602, 603 and 604) they are able to enter the diploma program, with a specialization in Business Analytics, and complete the following courses:
Overview of the basic concepts and techniques in predictive analytics as well as their applications for solving real-life business problems in marketing, finance, and other areas. Techniques covered in this course include: decision trees, classification rules, association rules, clustering, support vector machines, instance-based learning. Examples and cases are discussed to gain hands-on experience.
Introduces fundamental concepts and modelling approaches to solve problems that are faced by decision makers in today’s fast-paced and data-rich business environment. Different decision alternatives are analyzed and evaluated with the use of computer models. Topics include the most commonly used applied optimization, simulation and decision analysis techniques. Extensive use will be made of appropriate computer software for problem solving, principally with spreadsheets.
Introduction to new tools for data analytics that can be used to discover, collect, organize, and clean the data to make it ready for analysis. Emphasis is placed on software tools used to interact with data sources and provision of user skills to create business applications that encompass a variety of business data sources; such as customers, suppliers, markets, competitors, and regulators. Software packages used to clean and organize the data for analysis will be introduced, as well as software to enable users’ understanding of the data that is collected.
Examination of tools and methods used in data analysis, including basic and advanced analytic tools, as well as machine learning techniques. One or more data analysis packages/programs are used to analyze different types of business data. Statistical and other analytic methods, such as data mining, machine learning and various techniques, and their application to business data analytics are explored.