NOTE: PLEASE DO NOT REGISTER UNTIL YOU HAVE BEEN ADVISED. EMAIL Dr. CARBONARA or Dr. MIX.
| COURSE | FORMAT | TIME | CRN | FACULTY |
| DSA 601 | In Person | W 4:30 - 7:15 | 1917 | Matt Nagowski, Sumanlata Ghosh |
| DSA 610 | In Person | M 6:00 - 8:40 | 1970 | Betsy McCall |
| DSA 650 | Hybrid | M 6:00 - 8:40 | 1902 | Amin Serehali |
| MAT 646 | In Person | T-Th 4:30-5:45 | 2618 | Dr. Chaitali Ghosh |
| MAT 616 | In Person | T-Th 4:30-5:45 | 2619 | Dr. Joaquin Carbonara |
| PSM 602 | In Person | Th 6:00-8:40 | 1461 | Dr. David Aragona |
| HEA 730 | Asynchronous | 2115 | Dr Patrick McDonald | |
| DSA 688 | Asynchronous | Th 6:00-8:40 | 3206 | Dr. Joaquin Carbonara |
| DSA 587 | In Person | Th 6:00-8:40 | 2713 | Dr. Zhen Liu |
Prerequisites: MAT 126, MAT 311, CIS 512 OR INSTRUCTOR PERMISSION
Course Description: Applied introduction to building predictive, machine-learning models for real-world problems; learning Python computing environment, basic data analysis, management; data visualization and reporting using machine learning methods, including k-nearest neighbor, linear models, naive Bayesian models, decision trees, random forests, and neural networks. Sample data sets from across industry professions.
Prerequisites: None.
Course Description: Introduction to a “big picture” understanding of data flow for strategic, data-driven decision making, including data storage, data organization, data gathering and preparation, exploratory data analysis, and meaningful visualizations and communication. Includes hands-on practice. Offered occasionally, beginning spring 2020.
Prerequisites: None.
Format: HY; ONLINE ACTIVITY MIXED WITH CLASSROOM MEETINGS, REPLACING AT LEAST 20 PERCENT, BUT NOT ALL, REQUIRED ON-SITE MEETINGS.
Course Description: Elements, methods and tools of an organization’s data strategy and its governance. Components of a data strategy for each phase in the data lifecycle, tools for executing the strategy, and aligning the data strategy with the emerging needs of the organization. Policies, procedures, standards, and training for establishing authority over the ownership and use of data assets and its security.
Prerequisites: Graduate status. THIS SECTION REQUIRES INSTRUCTOR PERMISSION
Course Description: This course will have 3 components; internship, professional lab, and seminar. 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.
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: Instructor Permission
Course Description: 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-level standing.
Course Description: Developing strategic thinking on communication of quantitative information; improving writing, presentation, and interpersonal communication skills for mathematicians and scientists in a variety of settings (i.e. industrial, managerial, academic, research). Includes a review of “best practices” or guidelines that have been derived from both research and experience. Students will put those guidelines into practice, using a workshop format that will rely heavily on discussion and in-class exercises.
Prerequisites: None.
Course Description: 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: None.
Course Description: This elective course delves into real-world investment challenges, exploring the practical application of recently matured artificial intelligence tools. It covers fundamental concepts of financial markets, including both fundamental and technical analysis, alongside practical investment considerations. The course introduces crucial computing tools like Python libraries, data APIs, and back-testing platforms through case studies. In the later section, students learn to integrate artificial intelligence into investment practices using Data Science tools. This includes gaining proficiency in collecting, analyzing, and predicting data using Machine Learning and LLMs tools.
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