5 Data Science Course Programs in Canada

Data Science program leads to an undergraduate degree which provides graduates with the necessary mathematical and computational background to be a data scientist. It will introduce you to the applications of data science in your area of interest. Obtaining the degree will involve a combination of courses and experiential learning components in the form of a research paper, practicums and internships. Whether you are interested in precision medicine, dealing with data sets on environment or business analytics, or understanding complex systems, you will learn how to use the tools and problem-solving skills of computer science and mathematics to work with data.

Data Science is the emerging interdisciplinary study and application of how we capture, organize, archive, access, and use large-scale data. Data Science is a burgeoning field at the intersection of mathematics, statistics, computer coding, and critical thinking. It aims to draw insights from large databases available in the modern era through various processing techniques, statistical analysis, mathematical modelling, and data visualization.

Ready to level up? Our full-time Data Science boot camp gives you the right training, one-on-one coaching, and employer connections to land a data science career in industries like public policy, robotics, and fintech. Data scientists extract information from data, generating insights that help companies improve operational efficiency, find new business opportunities, and improve marketing and sales programs.

Our Data Science Short Course gives you the confidence to create robust predictive models with statistics and Python programming and tackle complex machine learning problems on the job. Learn to synthesize large data sets, tell compelling stories to stakeholders, and achieve new levels of data-driven success.

The course will cover Data Analytics and Information Systems for Accounting and Finance. The course will cover system concepts, information representation for decision making, value of information, organizational system needs to meet objectives, quality of information for decision making, data and information modelling, management of information systems infrastructure and architecture, system life cycle, and risks and controls.