This course provides a comprehensive understanding of data and its various forms. Students will explore different types and sources of data, and learn how to read, write, and store both structured and unstructured data.

The course also examines key challenges related to data storage and usage, helping students identify and address common issues in managing data effectively.

Databases are used to store data in a structured way and most organizations today capture their data using one or more databases. This course will introduce students to the concepts of relational database and how to apply fundamental knowledge of Structured Query Language (SQL) for storing, retrieving, and performing complex database operations in order to generate business insight.

Microsoft Excel is the most widely used spreadsheet application in the market. An easy but powerful tool for extracting actionable insights from data, Microsoft Excel skills are highly valued by employers. In this course, students would learn how to use Microsoft Excel for Data entry, calculation, data analysis and visualization.

This course covers the hardware and software foundations used in modern computing environments for data analysts.

Statistics involves the systematic collection, analysis, interpretation, and presentation of data. It provides a set of techniques for transforming data into meaningful information.

In this course, students will be introduced to statistical thinking and the key techniques required for effective data analysis.

Python is a general-purpose programming language that is easy to learn and versatile with rich library for data analysis. In this course, students will learn how to solve a broad set of data analysis problems effectively using the Python programming language.

The Data Analytics Process provides a framework that helps us to effectively explore data in order to provide valuable and actionable information. This course will introduce students to the main steps in the data analytic process.

During this course, the student will learn to apply visualization principles to the data analysis process. We will explore data visually at multiple levels to find insights and create a compelling story using the data.

This course introduces the mathematical foundations underlying machine learning. Topics include linear algebra, probability, and calculus, providing a refresher on essential mathematical concepts.

By the end of the course, students will have developed the prerequisite knowledge needed to pursue more advanced studies in machine learning.

This course will introduce the foundations of creating pipelines to efficiently extract, transform, and load data from multiple sources and into commonly used systems and formats.

This course introduces students to the key concepts underpinning artificial intelligence (AI), including its historical development and modern applications.

Students will explore how various technologies leverage AI in today’s society and examine the key differences between AI and machine learning. The course also covers data science, its relationship with AI, and the different types of machine learning.

This project is designed to demonstrate students’ understanding of the Data Analytics and AI program. Students will research and define a data problem, then apply a machine learning solution to address it.

Throughout the project, students will manage the workflow end-to-end, applying the knowledge and skills gained during the program. A technical report outlining the approach, analysis, and results will be submitted alongside the developed Python code, which may be provided as a document or through a Git repository.

Our Career Management course will introduce students to and teach them how to apply the most effective strategies to achieve employment in the career of their choice. Topics that are covered; effective job search strategies, cover letter and strategic resume writing, interview preparation and effective interviewing skills. 

Based on the path selected, the student will either complete a 16-week practicum placement or a capstone project.

Practicum

The 8-week practicum placement is designed as the culmination of the program. The Willis Career Services team will support you in securing a suitable placement with a business, public, or non-profit organization that operates according to standard Canadian business practices.

During your practicum, you may engage in hands-on work, job shadowing, or a combination of both, allowing you to apply the skills and knowledge gained throughout your program. While not all placements will provide opportunities to practice every skill learned, students are encouraged to observe and apply their learning wherever possible.

Capstone

The capstone enables students to apply their business intelligence and data analytics skills to a comprehensive, real-world project. You'll build a complete data pipeline from ETL to analysis, visualization, and reporting, delivering actionable insights for a business scenario.

The project showcases both the technical and strategic skills needed to drive data-informed decision-making in a business context.