Curriculum

Curriculum

The curriculum for Binghamton University's Master of Science in Data Analytics program combines core courses, practicums with real-world projects and electives from multiple fields of interest.

The highly quantitative nature of the 30-credit curriculum, which takes about 10 months to complete, qualifies the program as a STEM degree.

Plan of study

On-campus students can complete the program in about 10 months, with classes beginning during the fall semester and ending with the completion of summer term I.

View Binghamton University's academic calendar here.

Students will be assigned a faculty academic advisor, and will be required to meet with their advisor each semester to review progress in courses, field placement and career goals.

Fall

Students will complete DATA 500 during the first two weeks of the fall semester, while the other three courses will start in Week Three.

Winter

Spring

    • DATA 503: Applied Optimization and Decision Analytics

      Analytical models are key to understanding data, gaining insights into systems, generating predictions, and making decisions. Three major parts of analytical modeling are descriptive analytics (describe what happened), predictive analytics (predict what will happen) and prescriptive analytics (prescribe what should happen). In this course, we will discuss some modeling techniques for predictive and prescriptive analytics. This course introduces the students to the art of mathematical modeling of business and social systems for making practical, data-driven decisions. The methods covered include deterministic and stochastic optimization techniques, and simulation modeling techniques to discover and analyze the risk and uncertainty.

    • DATA 510: Analytics Practicum I

      This course teaches data analytics within a problem-solving framework. In doing so, students are provided a unique opportunity to apply the analytical tools and concepts taught in the program in a practical manner. Students will work on live projects from various organizations. Each project will have three to five students assembled as a team. Each project involves a single "client" organization, which may be a profit, non-profit or governmental organization. Each client provides its assigned study team with a project of current interest and an executive dedicated to working with the team. A faculty advisor is assigned to each team. Several faculty advisors might participate, depending on the expertise needed. Students schedule their own time, dovetailing with client schedules and that of their faculty advisor. Students (in consultation with the client and faculty advisor) will be responsible for project scope, understanding the issues and analytic needs, identifying appropriate analytical methods, analyzing the data, drawing conclusions, making recommendations for decision-making, writing a report and presenting conclusions/recommendations to the clients and to the advisor/instructor.

    • Elective
      See more info on electives below.
    • Elective
      See more info on electives below.

    Summer Term I

      • DATA 511: Analytics Practicum II

        This course teaches data analytics within a problem-solving framework. In doing so, students are provided a unique opportunity to apply the analytical tools and concepts taught in the program in a practical manner. Students will work on live projects from various organizations. Each project will have three to five students assembled as a team. Each project involves a single "client" organization, which may be a profit, non-profit or governmental organization. Each client provides its assigned study team with a project of current interest and an executive dedicated to working with the team. A faculty advisor is assigned to each team. Several faculty advisors might participate, depending on the expertise needed. Students schedule their own time, dovetailing with client schedules and that of their faculty advisor. Students (in consultation with the client and faculty advisor) will be responsible for project scope, understanding the issues and analytic needs, identifying appropriate analytical methods, analyzing the data, drawing conclusions, making recommendations for decision-making, writing a report and presenting conclusions/recommendations to the clients and to the advisor/instructor. 

      Coursework

      The program involves six core courses, two practicums and electives.

      Core courses

      The core courses ensure students have a confident grasp on the most relevant and important topics and concepts in data analytics, including regression, machine learning and data mining, modeling, databases and large data repositories.

      Practicums

      The two practicums involve team-based data analytics projects in collaboration with real-world organizations. This ensures that students understand the material through the framework of problem-solving, allowing them to put their knowledge and skills to the test through hands-on projects. Learn more about projects at this link.

      Electives

      Electives are rooted in today’s most pressing data analysis topics. Our students help drive the direction of the curriculum. As the program progresses, students work together to decide which electives will be taught, choosing from a number of options.

      Please contact us at msda@binghamton.edu with any questions.