Program Requirements
The 15-credit Graduate Certificate in Data Analysis for Complex Systems is designed to equip learners who do not necessarily have a technical background with the fundamentals of complex systems. The program focusses on applying data analysis techniques to better understand different phenomena in fields such as financial technology, organizational management, or digital marketing. The program is offered online with synchronous course activities.
Required Courses (9 credits)
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CCCS 610 Digital Thinking for Data Analysis (3 credits)
Overview
Computer Science (CCE) : Examination of programmable tools for data analysis. Application methods to implement fundamental algorithms making use of data structures.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
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CCCS 620 Data Analysis and Modelling
(3 credits)
Overview
Computer Science (CCE) : Analysis of fundamental concepts and techniques for data analysis and modelling; basic mathematical and computational concepts needed for data analysis. Application of programmable tools to real-world examples.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Not open to students who have taken CCS2 505.
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CCCS 630 Complex Systems (3 credits)
Overview
Computer Science (CCE) : Tools and techniques for modelling and simulating diverse complex systems; network analysis with applications in social, biological, and infrastructure networks; and chaotic systems.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Complementary Courses (6 credits)
6 credits from:
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CCCS 670 Information Visualization (3 credits)
Overview
Computer Science (CCE) : Examination of the application of computational and mathematical concepts, tools, and techniques to visualize quantitative multi-dimensional information. Qualitative information in static, animated, and interactive digital formats. Guidelines and best practices from cognitive psychology, UX, and graphic design, including dashboard tools and storytelling methods.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
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CCCS 680 Scalable Data Analysis (3 credits)
Overview
Computer Science (CCE) : Concepts, tools, and metrics related to scaling up data analysis to handle massive amounts of data. Examination of methods for enabling technologies and applications such as big data and cloud computing.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Not open to students who have taken CMIS 550.
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CCCS 690 Applied Computational Research (3 credits)
Overview
Computer Science (CCE) : Analysis of a real-world case study of choice. Examination of version-controlled repositories and project management tools for tracking the tasks. Identification, formulation, and application of data-driven projects in areas of interest.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Or another 600-level course offered by the School of Continuing Studies and approved by the academic unit.
