Master of Science in Analytics

Four students in a breakout room at the unh analytics building observing a chart on the large television on the wall

M.S. in Analytics - Program Overview

*In Case You Missed It: View our M.S. in Analytics Information Session.


May 2018 - May 2019
  spa     May 2019 - May 2020

The application process has closed for this cohort. Please contact with any questions.


Applications Accepted on a rolling basis.
Priority Consideration begins in October.
International applicants should allow 3-6 months prior to April 30 Deadline.
Program start date is in May 2019.


Take the next step. Schedule a site tour, apply or get more information today via email:, phone: 603.781.1832 or web links below.

These days, big data is a big deal, and it is everywhere - from professional sports to healthcare, and from e-commerce to government sectors. As the use of analytics grows, so does the demand for people who know how to skillfully extract value from massive amounts of data. The University of New Hampshire’s Master of Science in Analytics degree prepares graduates to fill the current gap in the marketplace. Analytics experts are needed - and in just 11 months, you can become one of them. The GRE is not required for this program.

The Master of Science in Analytics is a full-time, three-semester program (summer, fall, spring) at UNH is an interdisciplinary graduate program that offers students in-depth training in quantitative analysis, applications and reasoning, critical thinking and analytics. It has a cohort design and is a full-time program intended to engage students in real-world projects with external partnerships that provide them with unique opportunities to apply their skills, solve real-world analytics problems, and develop connections for employment. Students are able to specialize in areas of health, business, or self-designed focus areas. Professional development, critical thinking, presentation/communication, and leadership skills are integrated into the analytics program-one of the fastest growing fields in the world. 






Read Student Testimony




View a Recorded
Info Session





Program Highlights

  • Interdisciplinary approach with the opportunity to specialize in healthcare, marketing, operations management, or self-design study.
  • Work in analytics teams on projects sponsored by our industry partners.
  • Gain expertise in advanced predictive modeling, market segmentation and text analysis.
  • Master programming languages like Python, SQL, R and SAS, and become fluent in big data frameworks like Hadoop and MapReduce.
  • Intensive, full-time, on-campus program provides a specialized set of skills in just 1 year of study.

The MS in Analytics program provides a strong link to the professional world  through faculty, practicum projects and job placement training. Our students are immersed in the application of skills from beginning to end, and our curriculum remains relevant to the changing uses and technology of analytics.

Students will learn programming languages like Python,  R and SQL, so that they can derive actionable information from data. Students will have exposure to SAS and become fluent in big-data frameworks like Hadoop and MapReduce.

"I feel that my prospects to land a great job and one I will enjoy are greatly increasing, especially so with the decision science part of the program. Honestly, I couldn't have picked a better program." - Derek, class of '16.

MS in Analytics - Curriculum Overview

The MS Analytics - Program requires 36 credits. There are three, 12-credit semesters. Courses are 3 credits each. Two cluster course electives are required. The core Analytics curriculum is taught daily from 9am to 1pm.

A schedule could look like this:
  • Summer: 800, 801, 802, 803 (total: 12 credits)
  • Fall: 900, 901, 911, cluster (total: 12 credits)
  • Spring: 902, 903, 912, cluster (total: 12 credits)

Flow of the MS in Analytics Program

The Master of Science in Analytics at UNH begins in May with a statistics and linear algebra primer and continues through the summer, fall and spring semesters. This program will also be practicum-driven throughout the fall and spring semesters, with students working in teams to complete an industry or government sponsored real-world analytic problem.

Summer (Beginner Analytics)

The initial semester, brings together both the Graduate Certificate in Analytics (GCA) students and the M.S. students, to learn side by side. The summer semester provides students a measurable and consistent foundation in statistics and an overview of analytic foundations, tools and an exposure to their application. The session is an intense introduction to the field of analytics where students are using provided data to sample tools and techniques, get exposure to the field and engage in some high level hands on manipulation and presentation.

Each day, students will begin with instruction in such topics as data exploration, programming and data management, multivariate and logistic regression or data mining and spend the remainder of the day working on homework and project assignments.

Fall and Spring (Intermediate and Advanced Analytics)

These semesters mirror one another.  Students spend their mornings in class and in the afternoon collaborating in groups on projects, professional development and networking with industry partners/sponsors.  Building on the knowledge the gained from the summer they work toward a formal presentation in a practicum course, which acts as an academic yearlong capstone experience.

Both semesters consist of two analytics core courses and a practicum course. The core courses begin in the fall with data architecture and an intermediate exposure to Analytics Applications I. In the spring semester, students begin more advanced work in analytic methods and Analytics Applications II.

The practicum courses (Practicum I and II) are designed to instruct on two primary areas of content. One is to apply the core tools to the project the team is working on. The second is the professional nature of the presentation back to the sponsor and the professional development of the student in the role of analytics professional. Students will receive training on presentation skills, conceptual mapping of questions, conveying of data and analytic limitations and project scoping, as well as communication, messaging, and professional development skills such as resume writing, online presence and exposure to the industry of analytics and data science. They will also be paired with a faculty mentor to help counsel and instruct them during the project.


See some Practicum presentations here.


Read Testimonies Here.

Cluster Areas of Focus

The Cluster Course electives consists of two required courses, taken in the fall and spring semesters. See Clusters listed in the Sample Curriculum Map.

The final curriculum objective is to allow for specialization in a targeted area of student interest to provide students with a deeper knowledge in the subject area of their choice.

Current cluster options include health, accounting, decision science, finance, marketing, economics, sports, human & technology interface, or self-designed focus.


Some of our classes are supported by DataCamp, the most intuitive learning platform for data science.