A student points out a chemistry model simulation to a professor in the Taylor Family Digital Library

Diploma - Data Science

Application Portal Information


Fall 2024

  • November 1, 2023:  Application start
  • March 26, 2024:  Deadline for International applicants
  • July 12, 2024:  Deadline for Canadian/Permanent Residents

Lunch and Learn

Join our Lunch and Learn to hear more about our professional programs in the rapidly evolving fields of Quantum Computing, Data Science and Analytics and Information Security. 

Our one hour session will cover all three of our professional programs and a complimentary lunch will be provided. Faculty members will also be on hand to meet prospective students and answer questions.

Next Lunch and Learn:

Date: Thursday, June 20, 2024
Location: University of Calgary, Downtown Campus
Address: 906 8 Ave SW, Calgary, 
Time: 12 - 1 p.m.


Why the Diploma in Data Science?

Data Science professionals are increasingly in demand across all industries. Develop and hone your skills for your career in Data Science, and earn a Data Science Specialization Diploma.

Students in this specialization will learn how to apply the tools of data science and analytics - such as data analysis and cleaning, machine learning and information visualization. 

Possible career paths

Holders of a Data Science Specialization Diploma will function as a data scientist in the areas of health science, public sector, industry, or entertainment with an emphasis on algorithm creation and refinement to build better predictive models.

Similar career paths exist in the emerging fields of sports analytics and e-commerce that require the gathering, management, and analysis of large and diverse amounts of data to support data-driven strategic decision making.

Certificate courses

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 Data Science, and complete the following courses:

Introduces deeper tools, skills, and techniques for collecting, manipulating, visualizing, analyzing, and presenting a number of different common types of data. With a data life-cycle perspective, looks into data elicitation and preparation as well as the actual usage of data in a decision-making context. Introduces techniques for visualizing and supporting the interactive analysis and decision making on large complex datasets. Focus on critical thinking and good analysis practices to avoid cognitive biases when designing, thinking, analyzing, and making decisions based on data.

Design of surveys and data collection, bias and efficiency of surveys. Sampling weights and variance estimation. Multi-way contingency tables and introduction to generalized linear models with emphasis on applications.

Advancement of the linear statistical model including introduction to data transformation methods, classification, model assessment and selection. Exposure to both supervised learning and unsupervised learning.

Provides advanced coverage of tools and techniques for big data management and for processing, mining, and building applications that leverage large datasets. Addresses database and distributed storage design for both SQL and NoSQL systems, and focuses on the application of distributed computing tools to perform data integration, apply machine learning, and build applications that leverage big data. Students will also examine the security and ethical implications of large-scale data collection and analysis.

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