Data Science and Statistics (MS)

2 Year On Campus Masters Program

Binghamton University SUNY

Program Overview

The Master of Science in Data Science and Statistics at Binghamton University offers an industry-focused education combining statistical theory, computational science, machine learning, and practical data analysis to prepare students for professional careers in data science and statistics. Designed for students with strong quantitative backgrounds, the programme develops the skills needed to analyse complex datasets, build predictive models, apply advanced statistical techniques, and make data-driven decisions across industries such as finance, healthcare, engineering, technology, and research.

Curriculum Structure

The programme requires 40 credits across 10 graduate-level courses, including foundational subjects, specialised electives, practical data analysis, and a capstone seminar.

Year 1 – Foundational Coursework: Students develop a strong foundation in probability, statistical inference, computational mathematics, and regression analysis. Core courses include Probability (MATH 501), Statistical Inference (MATH 502), and Computational Linear Algebra (MATH 530), helping students understand statistical reasoning, mathematical computation, and techniques for analysing complex datasets.

Year 2 – Advanced Coursework and Specialisation: Students strengthen their expertise through advanced electives in statistical analysis, machine learning, and computational sciences. Subjects include Advanced Statistical Learning (MATH 535), Computational Statistics (MATH 543), and Data Science Principles with R (MATH 545), covering predictive modelling, statistical programming, machine learning techniques, and data-driven problem-solving.

Practical Training and Capstone: Students apply their knowledge through Practical Data Analysis (MATH 534) and Capstone Seminar (MATH 540). These courses develop practical experience with contemporary data analysis methods, statistical software, applied projects, and presentations, helping students communicate findings and solve real-world analytical problems.

Curriculum note: The university does not prescribe a fixed year-by-year course sequence. Students may complete the programme in three regular semesters or take four semesters, depending on their course planning and availability.

Focus Areas

Data Science, Statistics, Probability, Statistical Inference, Computational Linear Algebra, Statistical Modelling, Regression Analysis, Machine Learning, Advanced Statistical Learning, Data Mining, Computational Statistics, Scientific Computing, Data Analysis, Data Visualisation, Multivariable Analysis, Bayesian Statistics, Experimental Design, Time Series Analysis, Statistical Programming, Predictive Analytics.

Learning Outcomes

Apply probability and statistical inference techniques to analyse complex datasets; develop statistical models and perform regression analysis; use computational linear algebra and numerical methods to solve analytical problems; implement machine learning and advanced statistical learning techniques; apply data mining methods to identify patterns and relationships; use R and Python for statistical programming and scientific computing; conduct practical data analysis using contemporary software and methodologies; interpret and communicate statistical findings; develop data-driven solutions through applied projects and presentations.

Professional Alignment (Accreditation)

Binghamton University is institutionally accredited by the Middle States Commission on Higher Education (MSCHE) and has held accreditation since 1952. The MS in Data Science and Statistics is also a STEM-designated programme, making it relevant to international students seeking STEM-related employment opportunities in the United States. No separate professional accreditation specifically for this master's programme is listed by the university.

Reputation (Employability Rankings)

University Recognition: Binghamton University was ranked No. 1 among SUNY institutions for graduate salaries in the Wall Street Journal's 2025 America's Best Colleges recognition.
Faculty Expertise: 100% of programme instruction is delivered by tenured or tenure-track faculty members.
Industry Opportunities: Programme alumni have secured positions at organisations such as Amazon, Google, TikTok, AbbVie, Wells Fargo, Corning, and JPMorgan Chase.
Career Preparation: Students gain practical experience through data analysis courses, capstone projects, faculty guidance, and departmental seminars featuring researchers from academia and industry.
Career Opportunities: Graduates can pursue careers as data scientists, statisticians, statistical programmers, quantitative researchers, data analysts, and machine learning engineers.
Employment Outlook: The university highlights growing demand for data science professionals, with the US Bureau of Labor Statistics projecting 35% employment growth for data scientists between 2022 and 2032.
Programme Rankings: No specific QS or Guardian ranking for the individual MS in Data Science and Statistics programme is provided on the official university website.

Experiential Learning (Research, Projects, Internships etc.)

At Binghamton University, the Data Science and Statistics (MS) program is designed to provide students with hands-on experience that is essential for success in the field. Students have access to state-of-the-art facilities and cutting-edge tools that enhance their learning and prepare them for real-world challenges. The program emphasizes experiential learning through collaborative projects, internships, and the use of advanced software, ensuring that graduates are well-equipped with practical skills.

Here are some key aspects of experiential learning in the Data Science and Statistics program:

- Collaborative Projects: Engage in group projects that simulate real-world data analysis scenarios, allowing you to work alongside peers and faculty to solve complex problems.
- Internship Opportunities: Gain valuable industry experience through internships with local businesses and organizations, providing a chance to apply your skills in a professional setting.
- Advanced Software: Utilize industry-standard software such as R, Python, SAS, and Tableau for data analysis and visualization, ensuring you are proficient in tools that employers seek.
- Research Laboratories: Access dedicated research labs equipped with the latest technology, where you can conduct experiments and work on innovative projects under faculty supervision.
- Field Trips: Participate in field trips to local companies and research institutions, offering insights into how data science is applied in various industries.
- Libraries and Resources: Benefit from extensive library resources, including access to databases and journals that support your research and learning.
- Institute for Advanced Data Science: Collaborate with the Institute for Advanced Data Science, which focuses on interdisciplinary research and provides additional opportunities for hands-on learning.

These experiences not only enhance your academic journey but also build a strong foundation for your future career in data science and statistics.

Choosing Binghamton University for your graduate studies means you’ll be part of a vibrant community that values practical experience and prepares you for the challenges of the data-driven world.

Progression & Future Opportunities

Binghamton University's Data Science and Statistics (MS) program equips graduates with the skills and knowledge needed to excel in a rapidly growing field. Alumni have successfully secured positions in various sectors, including technology, finance, and healthcare. Typical job roles include Data Analyst, Data Scientist, and Statistician, showcasing the versatility and demand for expertise in data-driven decision-making:

- University Services: The Fleishman Center for Career and Professional Development offers personalized career counseling, resume workshops, and networking events tailored to data science students. Additionally, the university hosts career fairs that connect students with leading employers in the industry.

- Employment Stats and Salary Figures: Graduates from the Data Science and Statistics program have a high employment rate, with many securing jobs within six months of graduation. The average starting salary for data science professionals in the region is around $85,000, with potential for growth as experience increases.

- University–Industry Partnerships: Binghamton University has established partnerships with companies like IBM and Lockheed Martin, providing students with internship opportunities and real-world projects that enhance their learning experience.

- Long-term Accreditation Value: The program is accredited by the Middle States Commission on Higher Education, ensuring that the education you receive meets high standards and is recognized nationally.

- Graduation Outcomes: The program boasts a strong track record of graduates who go on to pursue successful careers or further academic opportunities, with many continuing their studies in PhD programs or specialized certifications.

Further Academic Progression: After completing the MS in Data Science and Statistics, students have the option to pursue a PhD in Data Science, Statistics, or a related field. This advanced study can open doors to academic positions, research roles, and higher-level industry positions. Additionally, graduates may choose to obtain professional certifications, such as Certified Analytics Professional (CAP) or specialized data science credentials, to further enhance their expertise and marketability in the job market.

Choosing Binghamton University for your graduate studies in Data Science and Statistics is a step toward a promising future filled with opportunities.