STUDENTS PLEASE DO NOT REGISTER UNTIL YOU HAVE BEEN ADVISED. YOU CAN EMAIL THE CHAIR OF DSA JOAQUIN CARBONARA, THE ASSOCIATE CHAIR, WENDE MIX OR THE PROGRAM COORDINATOR, HEATHER CAMPBELL.
| Class | Format | Time | CRN | Faculty |
| MAT 616 | In-person | TR 4:30 - 5:45 PM | 3994 | Dr. Joaquin Carbonara |
| MAT 646 | In person | TR 4:30 - 5:45 PM | 3932 | Dr. Chaitali Ghosh |
| CIS 512 | In person | W 4:30 - 7:10 PM | 2240 | Sumanlata Ghosh |
| CIS 512 | In person | R 6:00 - 8:40 PM | 4162 | Sumanlata Ghosh |
| CIS 600* | Online Synchronous | R 6:00-8:40 PM | NA | Dr. Reneta Barneva |
| PSM 601 | In person | T 6:00 8:40 PM | 2608 | Murray Richburg |
| COM 547 | In person | M 5:00-7:40 PM | 3783 | Dr. Ann Liao |
| HEA 730 (HEA 588) | Online Asynchronous | NA | 4043 | Dr. Patrick McDonald |
| DSA 621 | In person | MW 7:25 - 8:40 PM | 4100 | Dr. Nick Gilewski |
| DSA 690 | Asynchronous | NA | 2999 | Dr. Wende Mix |
*You will need to cross register for this course, it is being offerec at SUNY Fredonia
Prerequisites: Instructor Permission
Introductory topics in calculus, optimization, linear algebra and discrete mathematics useful for data scientists. Networking concepts relevant to data analytics approached from a mathematical point of view. Mathematical programming to implement a variety of numerical methods.
Prerequisites: Graduate Standing
Introduction to data analysis in Excel, Tableau, and Python; execute queries to extract data from a relational database; Data Science Life Cycle; tools and techniques to perform all the phases of the data science life cycle; Introduction to Machine Learning concepts.
Prerequisites: CIS 512 or DSA 512 or equivalent
Introduction to Machine Learning Techniques for Data Science; mathematical methods; algorithms; application to
academia, industry and business problems. Fundamental concepts and terms; methods, tools, and techniques. Supervised and unsupervised learning; identification of learning problems; data sources; analytical approaches; algorithm implementation; interpretation and reporting.
Prerequisites: Graduate standing
Current practices in project management as applied to math and science projects. Hands-on experience with the skills, tools, and techniques required in different phases of a project's life cycle, including project selection, project planning, project staffing and organization, task scheduling, project scope management, budgeting and progress reporting, risk management, quality management, project communications, and use of appropriate project management software tools. Techniques for communicating and motivating teams throughout the project life cycle. Emphasis on team building and practicing project management techniques through the use of science-based cases.
Prerequisites: Instructor Permission
Exploration of publicly available data and user-generated data; application of communication theory and research in data analytics; attention to tools and methods in communication data analytics; focus on ethical responsibilities of data scientists; intensive practice in using statistical software and Python.
Prerequisites: Graduate Standing
This course will have 2 components; internship and professional lab. The internship will acquaint the student with the specialized resources of various external organizations and will assist the student in understanding the nature of employment activities in offices/agencies that employ data scientists. The professional lab will provide the student with an opportunity to apply the skills and knowledge gained in the classroom to actual problems and tasks while still in an academic environment.
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