COmputer Science
The B.S. 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.
*Number of courses and course credit hours may vary, please reach out to your academic advisor for exact requirements.
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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.
Our fundamentals-based programs equip students with the knowledge and skills needed for today’s rapidly evolving careers. Our graduates have achieved remarkable success as entrepreneurs, key contributors in startups, and corporate leaders. Our alumni can be found in virtually every major tech company and throughout fields as diverse as finance, insurance, and defense. Student learning is supported through the Programming Assistance Center for introductory classes and through peer support for advanced courses. The department provides opportunities for students to augment their studies by engaging in research through projects and independent studies. Students have access to the UNH Interoperability Lab, where they have opportunities to work alongside top tech companies, and the UNH ECenter, where they can receive assistance with entrepreneurial ventures. Many of UNH’s student organizations, such as the Cybersecurity Club, are a natural fit for our students.
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.
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 |
| MATH 539 or MATH 644 | Introduction to Statistical Analysis or Statistics for Engineers and Scientists | 4 |
| Credits | 16 | |
| Second Year | ||
| Fall | ||
| CS 515 | Data Structures and Introduction to Algorithms | 4 |
| CS 674 | Fundamentals of Statistical Learning I | 4 |
| MATH 531 | Mathematical Proof | 4 |
| Discovery Lab | 4 | |
| Credits | 16 | |
| Spring | ||
| MATH 528 | Multidimensional Calculus | 4 |
| MATH 645 or MATH 545 | Linear Algebra for Applications or Introduction to Linear Algebra | 4 |
| CS 659 | Introduction to the Theory of Computation | 4 |
| Discovery Course | 4 | |
| Credits | 16 | |
| Third Year | ||
| Fall | ||
| CS 758 | Algorithms | 4 |
| IT 630 | Data Science and Scalable Data Systems | 4 |
| MATH 755 | Probability with Applications | 4 |
| Minor Elective I | 4 | |
| Credits | 16 | |
| Spring | ||
| CS 675 | Fundamentals of Statistical Learning II | 4 |
| MATH 756 | Principles of Statistical Inference | 4 |
| Minor Elective II | 4 | |
| Discovery Course | 4 | |
| Credits | 16 | |
| Fourth Year | ||
| Fall | ||
| CS 791 | Senior Project I | 2 |
| Minor Elective III | 4 | |
| Minor Elective IV | 4 | |
| Discovery Course | 4 | |
| Discovery Course | 4 | |
| Credits | 18 | |
| Spring | ||
| CS 792 | Senior Project II | 2 |
| Minor Elective V | 4 | |
| Discovery Course | 4 | |
| Discovery Course | 4 | |
| Credits | 14 | |
| Total Credits | 130 | |
All Major, Option and Elective Requirements as indicated.
*Major GPA requirements as indicated.
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.
The following courses or their equivalents must be passed with a grade of C- or better in order to meet the Data Science major requirements: CS 415, CS 416, CS 420, CS 457,and CS 515.
Students are expected to demonstrate consistent progress towards the satisfaction of the core degree requirements and are allowed two repeats of the aforementioned courses before being removed from the program. This can be a single class repeated twice or two classes repeated once. Students may petition to be reinstated after one year out of the program.
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 540 | Probability & Statistics | |
| 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 Scalable Data Systems | 4 |
| or CS 775 | Database Systems | |
| Capstone | ||
| CS 791 & CS 792 | Senior Project I and Senior Project II | 4 |
| or CS 799 | Thesis | |
| Minor | ||
| Select one of the following approved Minors: | 20 | |
Applied Mathematics Minor | ||
Statistics Minor | ||
Economics Minor | ||
Artificial Intelligence Minor | ||
| Total Credits | 98 | |
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 Data Science 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.