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.

• DATA 500 - Introduction to Data Analytics

  The course provides an overall introduction to the field of Data Analytics. Since date analytics involves concepts in analytic methods, programming tools, data retrieval and management, and applications in various domains, the course will provide concepts in these areas along with assignments that will allow students to learn how to make data-driven decisions. Ethical issues that shape data science activity (such as security, privacy, governance) in human contexts will also be covered. Credits: 3 Levels: Graduate

• DATA 501 - Predictive&Inferentl Analytics

  Data-driven decision-making abilities have become increasingly important to professionals and practitioners. This course aims to provide students with an in-depth understanding of multiple linear regression analysis, model diagnostics, model selection, logistic regression, analysis of variance (ANOVA), and related topics. Levels: Graduate

• DATA 502 - Machine Learning & Data Mining

  The challenge in analytics is to distill large amounts of data into useful information that has relevance for managerial decisions. Machine learning and data mining techniques provide solutions to this big data challenge. To illustrate, some recent applications of machine learning and data mining include (i) models to predict consumer preferences, (ii) models to detect fraudulent credit card transactions, and (iii) prediction of diseases in the medical diagnosis field. Students will learn how to apply these methods to solve real world problems. credits: 3 Levels: Graduate

• DATA 504 - Database & Lrg Data Repostries

  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, 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 as well as have 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. Levels: Graduate

Winter

• DATA 580A - Communicating & Visualizing Data

  This course covers personal presentation skills as well as how to present and communicate with data.

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.

Part-time students

The MS Data Analytics program offers a course sequence for part-time students that can be completed in two years. This allows flexibility to students who want to continue working full-time jobs while pursuing their degree.

Because the program is held in-person on the Binghamton campus, part-time students are required to attend in-person. Please note that most MS Data Analytics courses take place during the daytime, so students will need to have permission from their employer to allow time-off for courses and course-related activities such as group meetings.

Part-time course sequence

Year 1

• Fall: DATA 500, DATA 501
• Winter: DATA 580A
• Spring: DATA 503, Elective 1

Year 2

• Fall: DATA 502, DATA 504
• Spring: Practicum 1, Elective 2
• Summer: Practicum 2

To learn more about our part-time option, email msda@binghamton.edu.