STUDENTS PLEASE DO NOT REGISTER UNTIL YOU HAVE BEEN ADVISED. YOU CAN EMAIL Dr. JOAQUIN CARBONARA.
| COURSE | FORMAT | TIME | CRN | FACULTY |
| MAT 616 | In Person | TR 4:30 - 5:45 | 2310 | Dr. Joaquin Carbonara |
| MAT 646 | In Person | TR 4:30 - 5:45 | 2309 | Dr. Chaitali Ghosh |
| CIS 512 | In Person | W 4:30-7:10 | 1741 | Sumanlata Ghosh |
| INDS 589 (previously CIS600) | Online synchronous(starts the week of 8/26 like BSU; Thursdays 6 - 8 PM) | NA | must cross register | Dr. Reneta Barneva |
| PSM 601 | In Person | T 6:00- 8:40 | 1901 | Murray Richburg |
| DSA 501 | In Person | M 6:00 - 8:40 | 2645 | Dr. Joaquin Carbonara |
| HEA 730 | Online Asynchronous | NA | 3443 | Dr. Patrick McDonald |
| DSA 688 (previously DSA 690) | In Person | MW 04:30 pm - 05:45 pm | 3155 | STAFF |
*You will need to cross register for this course, it is being offered 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.
Prerequisite: Instructor permission.
Descriptive statistics, probability concepts, discrete and continuous probability distributions, sampling distributions, interval estimation and hypothesis testing of one and two population means, proportions and variances, non-parametric tests, simple linear regression and correlation, one-way analysis of variance.
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: 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.
Prerequisite: Instructor permission.
Practical hands-on introduction to Data Science and Data Analytics tools and acquiring, storing, manipulating, and exploring data - both big and small. Examples from bioinformatics (e.g., genomics), health care informatics, urban and regional planning, astronomy and data journalism. Extensive writing of formal reports.
Prerequisite: Instructor permission.
This course will cover the fundamentals of effective data-driven storytelling. Students will learn how to analyze data, detect stories within datasets and communicate findings in oral, written, and interactive visual delivery modes for various audiences.
Prerequisites: Graduate standing, instructor permission, and 3.0 minimum GPA
Internship and team project (a.k.a. professional labs). Internships acquaint students with specialized resources in industry. Professional labs allow students to apply skills to real-world challenges. Offered every semester, beginning spring 2024.
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