NOTE: PLEASE DO NOT REGISTER UNTIL YOU HAVE BEEN ADVISED. EMAIL Dr. CARBONARA or Dr. MIX.
Spring 2025 COURSESCOURSE
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
In person
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
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
DSA 601: MACHINE LEARNING MODELS IN PYTHON
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.
DSA 610: DATABASE & INFO CYCLE
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.
DSA 650: DATA STRATEGY & GOVERNANCE
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.
DSA 688: EXPERIENTIAL LEARNING IN DSA
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.
MAT 646 INTRODUCTION TO STATISTICS FOR DATA SCIENCE
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.
MAT 616: ELEMENTS OF MATHEMATICS, PROGRAMMING AND COMPUTER SCIENCE FOR DATA SCIENCE
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.
PSM 602: COMMUNICATION STRATEGIES FOR MATH AND SCIENCE PROFESSIONALS
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.
HEA 730: DATA VISUALIZATION AND STORYTELLING
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.
DSA 587: INTRODUCTION TO AI FOR INVESTMENT
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.