The B.S. in Data Science at UNH molds future leaders in the growing data revolution, crucial amid the global rise in data-driven advancements. Here, you’ll acquire advanced technical skills in building predictive models, data visualization and uncover patterns that guide strategic decisions across industries like finance, healthcare, and marketing in the region’s only on-campus offering in the field. You’ll develop strong technical skills in Python, R, and machine learning techniques such as neural networks and large language models essential tools in the new business economy. This program emphasizes extracting meaningful insights, preparing you for careers in various growing industries or advanced graduate programs.
What is the data science major?
This degree program will give you the skills to pursue original research or an advanced degree in data science. You’ll gain a deep understanding of concepts from mathematics and computer science such as probability theory, statistical analysis, predictive modeling, algorithms, machine learning, and data science.
Why study data science at UNH?
UNH was one of the first universities in the country to offer an undergraduate-level degree in analytics and data science. Our programs take a multidisciplinary approach that incorporates experiential education, professional development, projects and work experience. We partner with local businesses to give you the real-world experience that sets you apart, and our analytics majors have access to internships at many high-profile organizations. You’ll learn the cutting-edge technical skills you need to manage, distill, and interpret data for industries from finance to healthcare to marketing and advertising.
Potential careers
- Actuary
- Business analyst
- Consultant
- Data engineer
- Data scientist
- Management analyst
- Market research analyst
- Statistician
- Quantitative analyst
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Data Science Major Works With Company to Improve EfficiencyJulie Hilinski and a team of UNH students worked with When Pigs Fly bakery to build a more efficient distribution model for their bread company.Learn More -
Julia Hilinski is a senior analytics and data science major. For her senior project, she worked with the bread company, When Pigs Fly, to create an application that optimizes their wholesale distribution.Learn More -
Meet a Wildcat in Analytics & Data ScienceWatch Mallory Cashman discuss her favorite things about attending the University of New Hampshire and advice for those considering UNH.Learn More
Curriculum & Requirements
The BS in Data Science is intended for students interested in pursuing advanced degrees and conducting original research in data science. The program places its emphasis on a rigorous introduction to the theoretical mathematical and computational underpinnings of modern data science.
Sample Degree Plan
This sample degree plan serves as a general guide; students collaborate with their academic advisor to develop a personalized degree plan to meet their academic goals and program requirements.
| First Year | ||
|---|---|---|
| Fall | Credits | |
| CS 400 | Introduction to Computing | 2 |
| CS 415 | Introduction to Computer Science I | 4 |
| CS 457 | Introduction to Data Science and Analytics | 4 |
| MATH 425 | Calculus I | 4 |
| ENGL 401 | First-Year Writing | 4 |
| Credits | 18 | |
| Spring | ||
| CS 416 | Introduction to Computer Science II | 4 |
| CS 420 | Foundations of Programming for Digital Systems | 4 |
| MATH 426 | Calculus II | 4 |
| Discovery Course | 4 | |
| Credits | 16 | |
| Second Year | ||
| Fall | ||
| CS 515 | Data Structures and Introduction to Algorithms | 4 |
| MATH 531 | Mathematical Proof | 4 |
| MATH 645 or MATH 545 | Linear Algebra for Applications or Introduction to Linear Algebra | 4 |
| Discovery Lab | 4 | |
| Credits | 16 | |
| Spring | ||
| MATH 539 or MATH 644 | Introduction to Statistical Analysis or Statistics for Engineers and Scientists | 4 |
| MATH 528 | Multidimensional Calculus | 4 |
| Minor Elective I | 4 | |
| Discovery | 4 | |
| Credits | 16 | |
| Third Year | ||
| Fall | ||
| CS 659 | Introduction to the Theory of Computation | 4 |
| CS 674 | Fundamentals of Statistical Learning I | 4 |
| Minor Elective II | 4 | |
| Discovery | 4 | |
| Credits | 16 | |
| Spring | ||
| CS 758 | Algorithms | 4 |
| CS 675 or CS 750 | Fundamentals of Statistical Learning II or Machine Learning | 4 |
| Minor Elective III | 4 | |
| Discovery Course | 4 | |
| Credits | 16 | |
| Fourth Year | ||
| Fall | ||
| CS 791 | Senior Project I | 2 |
| MATH 755 | Probability with Applications | 4 |
| IT 630 | Data Science and Big Data Analytics | 4 |
| Minor Elective IV | 4 | |
| Discovery | 4 | |
| Credits | 18 | |
| Spring | ||
| CS 792 | Senior Project II | 2 |
| MATH 756 | Principles of Statistical Inference | 4 |
| Minor Elective V | 4 | |
| Discovery | 4 | |
| Credits | 14 | |
| Total Credits | 130 | |
Degree Requirements
All Major, Option and Elective Requirements as indicated.
*Major GPA requirements as indicated.
Major Requirements
Successful completion of the degree program includes earning a minimum of 128 credits, meeting the requirements of the University's Discovery Program, completing all of the 20 required courses in the major as listed below, capstone course, and a minor approved by the advisor.
In all major courses, a minimum grade of C- must be earned. The minimum overall GPA for graduation is 2.0.
Transfer students may transfer up to a maximum of 32 credits to satisfy major requirements (not counting those courses used to satisfy Discovery requirements).
| Code | Title | Credits |
|---|---|---|
| Required Courses | ||
| Mathematics | ||
| MATH 425 | Calculus I | 4 |
| MATH 426 | Calculus II | 4 |
| MATH 528 | Multidimensional Calculus | 4 |
| MATH 531 | Mathematical Proof | 4 |
| MATH 539 | Introduction to Statistical Analysis | 4 |
| or MATH 644 | Statistics for Engineers and Scientists | |
| MATH 645 | Linear Algebra for Applications | 4 |
| or MATH 545 | Introduction to Linear Algebra | |
| MATH 755 | Probability with Applications | 4 |
| MATH 756 | Principles of Statistical Inference | 4 |
| Computer Science | ||
| CS 400 | Introduction to Computing | 2 |
| CS 415 | Introduction to Computer Science I | 4 |
| or CS 410P | Introduction to Scientific Programming/Python | |
| CS 416 | Introduction to Computer Science II | 4 |
| CS 420 | Foundations of Programming for Digital Systems | 4 |
| CS 457 | Introduction to Data Science and Analytics | 4 |
| CS 515 | Data Structures and Introduction to Algorithms | 4 |
| CS 659 | Introduction to the Theory of Computation | 4 |
| CS 674 & CS 675 | Fundamentals of Statistical Learning I and Fundamentals of Statistical Learning II | 8 |
| or CS 674 & CS 750 | Fundamentals of Statistical Learning I and Machine Learning | |
| CS 758 | Algorithms | 4 |
| IT 630 | Data Science and Big Data Analytics | 4 |
| or CS 775 | Database Systems | |
| Capstone | ||
| CS 791 & CS 792 | Senior Project I and Senior Project II | 4 |
| or CS 799 | Thesis | |
| Select an Approved Minor 1 | ||
| Complete five (5) additional courses | 20 | |
| Total Credits | 98 | |
- 1
Minor must be approved by an academic advisor and must be in a discipline to which Analytics & Data Science can be applied (i.e. Economics or Applied Mathematics).
Instead of a minor, students may complete four (4) 600/700-level CS or MATH courses plus one (1) general elective. The additional general elective is required to meet the minimum 128 credits needed for graduation.
Program Learning Outcomes
- Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
- Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
- Apply theory, techniques, and tools throughout the data analysis lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs.
Explore Program Details
What is the difference between Analytics and Data Science on the Durham campus versus the Manchester campus?
The program is jointly administered across both campuses and the degree requirements are identical. The Durham campus is primarily for residential full-time students who can commit to classes throughout the day and week. The Manchester programs are more convenient for students who prefer classes in the evenings or in non-traditional formats. The classes for the Analytics option are available on both campuses, while some classes for the Data Science option are currently available only in Durham.
Those interested in the Analytics & Data Science: Data Science Option major may also be interested in the following advanced degrees. Students in the program also have the opportunity to participate in the UNH accelerated master’s program.
