FULL-TIME | WINNIPEG LOCATIONS ADSAF-DP Applied Data Science and Artificial Intelligence

• Two year diploma program (delivered over four terms)
• September and January intake options
• Exchange District Campus, Winnipeg
• Mandatory Work Integrated Learning (WIL) term (Co-op Work Experience or Industry Project)
• Spring terms are normally scheduled breaks with no classes. However, Work Integrated Learning (WIL) may occur during this period, and a limited number of courses may be offered based on student needs and at the discretion of the program.
• Classes typically run between 8:00 a.m. and 6:00 p.m. (please note that some classes may occasionally run later in the evening)
• Courses are delivered in a blended format, typically 50% on campus and 50% online/remote (the schedule may be updated periodically and will be communicated in advance)
• Students are required to provide their own laptop that meets the program’s technical requirements 
• International applicants please visit Academic Program, Dates and Fees for a listing of programs for international students, current availability and online application instructions.

Description:

As artificial intelligence, machine learning, and data-driven technologies continue to evolve, organizations need professionals who can turn information into insight, build practical solutions, and use technology responsibly. This exciting field requires technical and analytical ability as well as human skills such as curiosity, critical thinking, communication, collaboration, ethical judgment, and the ability to explain complex ideas. This program develops these abilities together, helping you gain the adaptable skills needed to remain future-ready as AI technologies and industry needs continue to change.

Applied Data Science and Artificial Intelligence is a two-year diploma program with an integrated co-op or industry project that prepares students to design and implement practical AI and data solutions. You will build a strong foundation in programming, mathematics, statistics, analytics, and machine learning while learning how data-driven systems can support better decisions, automation, and real-world problem-solving.

Through hands-on learning, you will gain experience collecting, managing, analyzing, and interpreting data using modern database, data-management, and data-architecture techniques. You will design and implement supervised, unsupervised, and deep-learning models and work with applied AI, generative AI, large language models, intelligent systems, automation technologies, and agentic workflows. The program also explores responsible and secure AI development, including privacy-aware design, data protection, data governance, data sovereignty, and ethical deployment. Elective options include private AI and data security, information and data architecture, neural networks and deep learning, and robotics and automation.

Graduates are prepared to pursue junior opportunities in data analysis, applied artificial intelligence, machine learning, business intelligence, and related data and technology roles, with a foundation that can support continued learning and career growth as the field develops.

Graduate Profile:

Graduates of the Applied Data Science and Artificial Intelligence program are prepared to:

• Analyze real-world problems and determine appropriate data science, machine learning, or artificial intelligence solutions based on the problem domain, available data, and desired outcomes.
• Develop software solutions using appropriate programming languages, tools, and frameworks to support data analysis, machine learning, and AI applications.
• Conduct research by reviewing technical literature, collaborating with stakeholders, and applying investigative methods to acquire domain knowledge and evaluate existing approaches.
• Prepare, manage, and structure data for analysis by applying data cleaning, transformation, and data management practices to support reliable and meaningful insights.
• Design, train, and evaluate machine learning models—including supervised, unsupervised, and deep learning approaches—to generate accurate and reliable predictions from data.
• Develop intelligent applications and automated systems that integrate data science, artificial intelligence, and software development practices.
• Verify and validate software and AI systems to ensure they meet requirements, perform reliably, and achieve their intended outcomes.
• Communicate technical concepts, results, and processes effectively to both technical and non-technical audiences using written, verbal, and visual communication methods.
• Apply responsible and ethical practices in the development and use of data and AI systems, including attention to privacy, security, and regulatory requirements.
• Demonstrate professionalism, integrity, accountability, and a commitment to continuous learning in a rapidly evolving technology field.