Master of Science in Data Science and Analytics (STEM)

State University of New York - Buffalo State University

USA,New York

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24 Months

Duration

CAD 18,600/year

Tuition Fee

CAD 65

Application Fee

Aug 2025

Apply Date

USA, New York

Type: University

Location Type: Urban

Founded: 1871

Total Students: 6,405 +

Campus Detail

Main Campus Address

1300 Elmwood Ave, Buffalo, NY 14222, United States

Master of Science in Data Science and Analytics (STEM)

Program Overview

The M.S. in Data Science and Analytics offers advanced practical training for in-demand modern skills to manipulate, organize and present data essential for informed, evidence-based decision-making and planning across industries. This program is open to students with basic programming and statistical skills from all undergraduate majors. Courses cover highly marketable techniques using data analytics tools, computer coding, machine learning, geospatial programming, data design, visualization, and analysis. Students will develop professional skills in project management, communications, data governance, and creative problem-solving for effective collaboration. Unique components of the program include workshops with local experts and an applied-skills internship with industry partners.

Learning Outcomes

Students will:

  • Select and apply an appropriate statistical, mathematical or computational model for a given quandary
  • Acquire data from data scraping and open sources and understand the ethical and legal ramifications of data acquisition
  • Store, clean, organize, and manipulate real world data from multiple sources
  • Compose and present an effective oral, written report or dynamic dashboard, to a lay audience (including storytelling and data visualization) that enhances the audience’s understanding and reveals properties of the data
  • Use the appropriate software or programming application (Python, SQL, SAS, SPSS, Excel) to manage and analyze data
  • Perform effectively as a member of a team to execute a project and will understand what contributes to team success
  • Integrate context specific information into their data manipulation allowing them the flexibility to interpret data from many different environments