The Analytics & Data Science: Analytics option at UNH prepares you to shape the future of data analysis leaders amidst a global surge in big data initiatives. As the sole on-campus program of its kind in the region, it primes you with essential skills to manage, distill, and interpret data across diverse sectors like finance, healthcare and marketing. Here, you’ll develop strong technical skills in Python, R, and big-data frameworks like Hadoop and MapReduce to derive actionable insights for tactical decision-making coveted by employers. The analytics option is designed to prepare you for an array of rapidly growing career fields or to pursue graduate education.
What is the analytics option in analytics and data science?
This option in the analytics and data science degree program gives you a solid foundation in the tools you’ll need for careers in the data science industry. In addition to courses in mathematics, computer science and analytics, you’ll study business organizations and behavior, and professional technical writing. Our graduates have the skills to start careers immediately after completing their degree, or to pursue further study in a professionally oriented program such as the Master of Science in Analytics at UNH.
Why study analytics and 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. The analytics option is available through both the Durham and Manchester campuses.
Potential careers
- Actuary
- Business analyst
- Consultant
- Data engineer
- Data scientist
- Management analyst
- Market research analyst
- Quantitative analyst
Curriculum & Requirements
The option in Analytics is intended for students interested in either heading into industry immediately upon graduation, or pursuing graduate work in a professionally oriented program such as the Master of Science in Analytics at UNH. The option in Analytics places its emphasis on applications of data science in business and industry.
Program Objectives
Analytics and Data Science focuses on the extraction of meaning from data through the application of computer science, mathematics and business domain knowledge. Within a few years of obtaining a bachelor's degree in Analytics and Data Science, our alumni will have:
- Engaged in successful career areas of analytics and data science and will already have, or be pursuing, advanced degrees in Analytics, Data Science, Computer Science, Mathematics or related fields
- Applied the full range of core Data Science concepts and techniques to fill the analytics needs of an organization
- Communicated effectively with diverse stakeholders as well as functioned appropriately in a team environment
- Navigated the complex interconnections between data, computing technology, and the goals and constraints of the organization served
- Understood the pervasive and changing role of data in global society, and participated responsibly as both an Analytics and Data Science professional and citizen
For additional information about the Analytics and Data Science: Analytics Option, contact Matt Magnusson, program co-director (Durham campus), or Jeremiah Johnson, program co-director (Manchester campus), at (603) 641-4127.
Sample Degree Plan
| 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 |
| MATH 426 | Calculus II | 4 |
| ADMN 400 | Introduction to Business | 4 |
| Discovery Course | 4 | |
| Credits | 16 | |
| Second Year | ||
| Fall | ||
| CS 515 | Data Structures and Introduction to Algorithms | 4 |
| IT 520 or CS 520 | Foundations of Information Technology or Computer Organization and System-Level Programming | 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 |
| ENGL 502 | Professional and Technical Writing | 4 |
| MGT 535 | Organizational Behavior | 4 |
| Discovery Lab | 4 | |
| Credits | 16 | |
| Third Year | ||
| Fall | ||
| DATA 674 | Predictive and Prescriptive Analytics I | 4 |
| IT 505 | Integrative Programming | 4 |
| MGT 540 | Leadership in the 21st Century | 4 |
| Discovery Course | 4 | |
| Credits | 16 | |
| Spring | ||
| DATA 690 | Internship Experience | 1-4 |
| Analytics Course II | 4 | |
| 600- or 700-level Elective I | 4 | |
| Discovery Course | 4 | |
| Credits | 13-16 | |
| Fourth Year | ||
| Fall | ||
| CS 791 | Senior Project I | 2 |
| MATH 739 | Applied Regression Analysis | 4 |
| CS 750 | Machine Learning | 4 |
| 600- or 700-level Elective II | 4 | |
| Discovery Course | 4 | |
| Credits | 18 | |
| Spring | ||
| CS 792 | Senior Project II | 2 |
| CS 775 | Database Systems | 4 |
| 600- or 700-level Elective III | 4 | |
| Discovery Course | 4 | |
| Credits | 14 | |
| Total Credits | 127-130 | |
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 24 required courses in the major as listed below, including the capstone course, the internship preparedness course, and a three-credit internship.
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 Program requirements).
| Code | Title | Credits |
|---|---|---|
| Required Courses | ||
| Mathematics | ||
| MATH 425 | Calculus I | 4 |
| MATH 426 | Calculus II | 4 |
| MATH 644 | Statistics for Engineers and Scientists | 4 |
| or COMP 570 | Statistics in Computing and Engineering | |
| or MATH 539 | Introduction to Statistical Analysis | |
| MATH 645 | Linear Algebra for Applications | 4 |
| or MATH 545 | Introduction to Linear Algebra | |
| MATH 739 | Applied Regression Analysis | 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 | |
| or COMP 424 | Applied Computing 1: Foundations of Programming | |
| CS 416 | Introduction to Computer Science II | 4 |
| or COMP 525 | Data Structures Fundamentals | |
| CS 457 | Introduction to Data Science and Analytics | 4 |
| or DATA 557 | Introduction to Data Science and Analytics | |
| CS 515 | Data Structures and Introduction to Algorithms | 4 |
| or COMP 625 | Data Structures and Algorithms | |
| IT 505 | Integrative Programming | 4 |
| or COMP 520 | Database Design and Development | |
| IT 520 | Foundations of Information Technology | 4 |
| or CS 520 | Computer Organization and System-Level Programming | |
| or COMP 430 | Systems Fundamentals | |
| Business | ||
| Selected in consultation with advisor: | 12 | |
One (1) course in Introduction to Business | ||
One (1) course in Organizational Behavior | ||
One (1) course in Organizational Leadership | ||
| English | ||
| ENGL 502 | Professional and Technical Writing | 4 |
| Analytics | ||
| DATA 674 & DATA #675 | Predictive and Prescriptive Analytics I and Predictive and Prescriptive Analytics II | 8 |
| or DATA 674 & CS 750 | Predictive and Prescriptive Analytics I and Machine Learning | |
| or MATH 738 & CS 750 | Data Mining and Predictive Analytics and Machine Learning | |
| DATA 690 | Internship Experience | 1-4 |
| COMP 721 | Big Data for Data Engineers 1 | 4 |
| Capstone | ||
| CS 791 & CS 792 | Senior Project I and Senior Project II | 4 |
| or CS 799 | Thesis | |
| Electives | ||
| Select three (3) 600 or 700-level elective courses approved by advisor. | 12 | |
| Total Credits | 91-94 | |
- 1
Or another suitable 700-level data science or data engineering course chosen in consultation with the program coordinator.
- 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: Analytics 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.
