Program Requirements
The 15-credit Graduate Certificate in Data-Driven Decision Making is designed to provide the fundamentals of computational intelligence focusing on leadership roles in increasingly digital organizations operating in the numerous fields that need to make data-driven decisions such as digital healthcare, maintenance of critical infrastructure, or dynamic supply management. The program is offered online with synchronous course activities.
Required Courses (9 credits)
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CCCS 640 Applied Decision Science (3 credits)
Overview
Computer Science (CCE) : Analysis of concepts, tools, and techniques provided by mathematical and computational sciences for decision making in its diverse formats. Examination of decision science techniques and their applicability.
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 CMS2 505.
Course may be offered in person or online with synchronous and asynchronous activities.
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CCCS 650 Applied Data Science (3 credits)
Overview
Computer Science (CCE) : Analysis of techniques provided by statistics and computational sciences for machine learning and data science. Examination of real-world applications.
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 CMS2 529.
Course includes synchronous activities.
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CCCS 660 Computational Intelligence (3 credits)
Overview
Computer Science (CCE) : Analysis of tools provided by mathematical and computational sciences for artificial intelligence as well as an examination of the numerous contexts in which computational intelligence can be applied.
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.
Course includes synchronous activities.
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.
Course includes synchronous activities.
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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.
Course includes synchronous activities.
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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 Continuous Studies and approved by the academic unit.
