Applied Data Science and Artificial Intelligence

What if the spreadsheets and sensor logs most companies ignore could train a model that makes useful decisions? RRC Polytech’s Applied Data Science and Artificial Intelligence diploma spends two years, over four terms, teaching you to build those systems at the Exchange District Campus downtown, where the next intake is listed at Manitou a bi Bii daziigae.

Term 1 builds the maths and code: Introduction to Programming Logic, Statistics for Data Science and Machine Learning, Linear Algebra for Data Science and Machine Learning and Introduction to Data Science and Machine Learning. Term 2 moves to Python Essentials With Data Analysis, Transforming Data Into Databases, Data Management and Supervised Machine Learning. Term 3 covers Applied Artificial Intelligence and Unsupervised Machine Learning, plus one elective from Neural Networks and Deep Learning, Robotics and Automation, Private AI and Data Security or Information and Data Architecture. Communication courses in the first three terms train you to explain model results to non-specialists.

Classes are blended, roughly half on campus and half online, and use Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn, SQL and NoSQL; the robotics elective introduces ROS2 and AWS RoboMaker, and later courses cover large language models and generative AI. The programme stresses responsible and secure AI development. The final term is a work-integrated learning choice: a paid 15-week co-op, or an industry project in the ACE Project Space, where multidisciplinary teams use Agile practices to build a technology solution proposed by an industry partner. You need a laptop meeting the programme’s higher specifications.

Graduates move into data analytics, applied AI development, machine learning support, intelligent systems implementation and data engineering, in roles such as data analyst, junior machine learning developer, AI solutions assistant, data engineer and business intelligence analyst. The diploma suits people comfortable with statistics and linear algebra who want to build and deploy models, not only read about them.