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

    • DATA 504: Databases and Large Data Repositories

      The focus of this course is on understanding information systems and infrastructure used in Data Analytics. The course will provide an introduction to elements of database design and database query languages. Students will also gain technical understanding of and hands-on experience with the information technology infrastructure required for data analytics. The first part of the course focuses on traditional databases and structured data. It covers association between data elements and data models (including entity-relationship and relational models), relational database design techniques and database query languages. Students will be exposed to the basics in query processing, transaction management and concurrency control. The second part of the course covers non-relational databases and big data infrastructure. Students compare and contrast and gain hands-on experience with various non-relational databases including document, graph and column databases. Students will also be exposed to Hadoop environment and basic services available in this environment, distributed file systems, storage and processing.

    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.

        Electives

        Students will be able to choose electives based on their proposed field of interest.

        Below is a sampling of some of the elective course offerings students may have the opportunity to choose from.

        We cannot guarantee whether or not an advertised elective course will be offered, and if so, when. Please contact us at msda@binghamton.edu with any questions.