Data Analytics (MS)

Program Overview

Binghamton University’s new Master of Science in Data Analytics degree program prepares students from diverse backgrounds with the analytical, quantitative, technical and leadership skills needed for successful careers in data analysis across all sectors of the economy.

Through an innovative collaboration between the Harpur College of Arts and Sciences, the School of Management and the Thomas J. Watson School of Engineering and Applied Science, this unique program gives students a cross-disciplinary, hands-on education that incorporates real-world projects and applications. The highly quantitative nature of the curriculum qualifies the program as a STEM degree.

The 30-credit MS Data Analytics program provides a robust exploration of the core fundamentals of analytics, while also allowing for the flexibility to explore applicable areas of interest and emphasizes real-world applications.

Students will take classes in summer term II, fall and spring semesters. Applicants with prior STEM degrees and work experience are preferred.

Degrees Offered

  • MS in Data Analytics

Internships, research opportunities and more

Students will experience hands-on learning through applied projects in various fields such as business, non-profits and healthcare. Working both independently and in teams of students, members of the program will have the opportunity to apply different analytics and methods to solve real data problems provided by corporate and organizational partners. These problems will be both business and nonbusiness related.

After You Graduate

Data is increasingly shaping the modern world, and there is a growing demand for people who can make sense of it. From Wall Street to manufacturing to healthcare to education, every industry has a need for individuals who not only have the technical skills, but the teamwork and communication skills necessary for successful analysis and decision making.

Students will be prepared for the growing need of analytics and intelligence in all sectors of the economy, and will be able to:

  • Demonstrate knowledge of appropriate analytics techniques, tools and software, including those in the big data regime and data visualization tools to communicate results of data analysis
  • Recognize the data structures, storage, retrieval and other technical needs in order to implement analytical solutions and make the results available throughout organizations
  • Develop and apply predictive models using statistical, data mining and machine learning techniques to solve real world business and decision problems
  • Work in teams with students of diverse backgrounds, present and communicate findings to leaders of project clients

Admissions Requirements

To be eligible for graduate study, you must:

  • Provide a complete set of your undergraduate (and, if applicable, graduate) transcripts showing one of the following:
    • You have earned a bachelor's degree (or its equivalent) from a nationally or regionally accredited college or university
    • You are within one academic year of earning a bachelor’s degree (or its equivalent) from a nationally or regionally accredited college or university
    • You are eligible to apply as part of a memorandum of understanding between your current institution and Binghamton University
  • Have earned, at minimum, one of the following:
    • A 3.0 GPA over your entire undergraduate career
    • A 3.0 GPA during your last 60 semester credits or 90 quarter credits of your undergraduate degree, with most courses graded regularly (not as "pass/fail")
    • A 3.0 GPA in a graduate degree, with most courses graded regularly (not as "pass/fail")
    • In consideration of the different grading scales used around the world, each academic department evaluates international transcripts to determine on a case-by-case basis whether they demonstrate one of the above requirements.
  • Have a bachelor’s or graduate degree in mathematics, statistics or an applied science such as economics, business, management science, computer science, system science or industrial engineering. Applicants with at least two years of work experience in a business or industrial setting will be preferred, and some requirements for the program may be substituted by relevant work experience in a related field.

To apply, you must submit the following materials. For general guidelines for these materials, see the Admission Requirements website.

  • Online graduate degree application with graduate degree application fee
  • Transcripts from each college or university that you have attended
    • A baccalaureate or graduate degree in mathematics, statistics, or an applied science such as economics, business, management science, computer science, system science or industrial engineering is required for admission.
  • Personal statement of 2 to 3 pages describing your reasons for pursuing graduate study, your career aspirations, your special interests within your field, and any unusual features of your background that might need explanation or be of interest to the graduate admissions committee
  • Résumé or curriculum vitae
  • Two letters of recommendation
  • Official GRE or GMAT scores

International students must also submit the following materials. For more information about these materials, see the International Students section of the Admission Requirements website.

  • International Student Financial Statement (ISFS) form
  • Supporting financial documentation (such as bank statements, scholarship or sponsor letters, etc.)
  • Proof of English proficiency (such as official TOEFL/IELTS/PTE Academic scores)
    • Data Analytics minimum TOEFL score: 100 on the iBT
    • Data Analytics minimum IELTS score: 7.0
    • Data Analytics minimum PTE Academic score: 65

This information is subject to change. While we make every effort to update these program pages, we recommend that you contact the department with questions about program-specific requirements.

For more information, contact msda@binghamton.edu

Deadlines

Degree Fall Spring
MS in Data AnalyticsRolling (March 15 Recommended)N/A

For more information, visit the Data Analytics website.