M.P.S. in Data Sciences and Applications

Program Overview

The M.P.S. in Data Sciences and Applications at UB is a 30-credit hour program, typically completed within 1 to 1.5 years for full-time students. It is a professional master's degree, emphasizing applied knowledge and skills rather than extensive theoretical research. The program focuses on training students in data manipulation, database management, distributed and big data management, and cloud-based methodologies. It is cohort-based for the initial semesters, ensuring a structured learning path. The program is designed to provide graduates with the expertise to obtain valuable insights from complex data effectively, preparing them for roles across various sectors.

STEM Designated: Yes, the M.P.S. in Data Sciences and Applications program at the University at Buffalo is STEM-designated. This classification is highly beneficial for international students, as it allows for an Optional Practical Training (OPT) extension of 24 months beyond the initial 12 months, totaling up to 36 months of work authorization in the U.S. after graduation. This designation highlights the program's rigorous technical and analytical curriculum.

Curriculum: The 30-credit curriculum for the M.P.S. in Data Sciences and Applications is structured to provide a strong foundation in core data science concepts and tools. Required courses (typically 18 credits) include topics such as programming and database fundamentals (e.g., Python, regression analysis), database management systems (SQL, ETL workflows), introduction to numerical analysis, and probability and data analysis. Students also take electives (12 credits) that allow for specialization in areas like machine learning models, geospatial programming, data visualization, or advanced statistical methods. The program culminates in a project or capstone experience, allowing students to apply their learned skills to real-world datasets.

Research Focus: While the M.P.S. in Data Sciences and Applications is primarily an applied, professional degree, it incorporates elements of research through its emphasis on data analysis, data mining, and machine learning techniques. Students learn to "execute mathematical and computing techniques commonly used for analyzing data" and gain expertise in obtaining insights from large and heterogeneous data sets. The culminating project often involves independent or team-based applied research on a specific data science problem, allowing students to demonstrate their ability to design, implement, and interpret data-driven solutions. The program's connection to the Institute for Artificial Intelligence and Data Science at UB further indicates its grounding in current research trends and methodologies.

Industry Engagement: The M.P.S. in Data Sciences and Applications program at UB is highly focused on preparing graduates for immediate and effective contributions to industry. The curriculum is developed with input from industry partners and aims to equip students with highly marketable techniques using various data analytics tools and programming applications (e.g., Python, SQL). The program often facilitates experiential learning opportunities, including internships with community organizations and industry partners. Additionally, a speaker series brings in industry experts to discuss cutting-edge topics in analytics, fostering direct engagement and networking opportunities with professionals in the field. This strong industry focus ensures graduates are well-prepared for real-world data science challenges.

Global Perspective: The M.P.S. in Data Sciences and Applications at UB inherently fosters a global perspective by addressing the universal need for data-driven decision-making across diverse industries worldwide. Data science principles and methodologies are globally applicable, and the program's curriculum prepares students to work with global datasets and solve problems with international implications, from business intelligence to healthcare analytics. As a major public research university, UB attracts a diverse international student body and faculty, enriching the classroom environment with varied cultural viewpoints and different approaches to data challenges from around the world. This exposure prepares graduates to contribute to a global workforce increasingly reliant on data expertise.