Graduate Certificate (Gr. Cert.) Data-Driven Decision Making (15 credits)

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.

Required Courses (9 credits)

• CCCS 640 Applied Decision Science (3 credits)

  Offered by: Technology & Innovation (School of Continuing Studies)

  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: Winter 2024

  Instructors: Kahyaoglu, Yasemin (Winter)

  • Not open to students who have taken CMS2 505.

• CCCS 650 Applied Data Science (3 credits)

  Offered by: Technology & Innovation (School of Continuing Studies)

  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: Winter 2024

  Instructors: Beitinjaneh, Nabil (Winter)

  • Not open to students who have taken CMS2 529.

• CCCS 660 Computational Intelligence (3 credits)

  Offered by: Technology & Innovation (School of Continuing Studies)

  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: Winter 2024

  Instructors: Schaeffer, Satu Elisa (Winter)

Complementary Courses (6 credits)

6 credits from:

• CCCS 670 Information Visualization (3 credits)

  Offered by: Technology & Innovation (School of Continuing Studies)

  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 2023-2024 academic year.

  Instructors: There are no professors associated with this course for the 2023-2024 academic year.

• CCCS 680 Scalable Data Analysis (3 credits)

  Offered by: Technology & Innovation (School of Continuing Studies)

  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: Summer 2024

  Instructors: Havas, Michael (Summer)

  • Not open to students who have taken CMIS 550.

• CCCS 690 Applied Computational Research (3 credits)

  Offered by: Technology & Innovation (School of Continuing Studies)

  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 2023-2024 academic year.

  Instructors: There are no professors associated with this course for the 2023-2024 academic year.

  • Prerequisites: CCCS 630 or CCCS 640

Or another 600-level course offered by the School of Continuous Studies and approved by the academic unit.