Analytics and Data Science Major: Data Science Option (B.S.) Manchester
https://manchester.unh.edu/program/bs/analytics-data-science-major-data-science-option
The option in Data Science is intended for students interested in pursuing advanced degrees and conducting original research in data science. The option in data science places its emphasis on a rigorous introduction to the theoretical mathematical and computational underpinnings of modern data science.
Program Objectives
This program has been designed to prepare students for professional careers working with data, with an emphasis on the extraction of meaning from data. The program is not targeted to any one industry; rather, it provides a flexible, practical skillset that can be applied widely. This skillset includes elements of computer science, applied mathematics and statistics, communication skills, and business savvy. Graduates of the bachelor of science in analytics and data science program are expected to have:
- An understanding of the role of data in guiding decision-making in industry
- An understanding of how data is generated, stored, and accessed
- An understanding of data security
- An understanding of the ethical use of data
- An understanding of structured vs. unstructured data
- An understanding of the methods, statistical and other, used to derive actionable information from data
- Experience with multiple programming languages
- Experience with multiple statistical and data analysis software programs
- The ability to communicate detailed, technical information to a variety of audiences clearly and concisely, without the use of jargon
- The ability to work effectively, both as an individual or as a member of a team
- The ability to successfully lead a team
- The ability to adapt to a dynamic, rapidly changing work environment
- Completed projects and other work experiences on a larger scale than is typical in a bachelor's degree program.
During the course of the program, students will demonstrate their acquisition of these skills by successfully completing their program coursework, their internship experience, and their capstone project.
Successful completion of the program entails earning at least 128 credits, meeting the requirements of the University's Discovery program, and completing all of the 18 required courses in the major as listed below. In all major courses, the minimum allowable grade is a C-. 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).
Students who enroll in the Data Science Option may need to take some required courses on the Durham campus.
Program Requirements
| Code | Title | Credits |
|---|---|---|
| Mathematics | ||
| MATH 425 | Calculus I | 4 |
| MATH 426 | Calculus II | 4 |
| MATH 528 | Multidimensional Calculus | 4 |
| MATH 531 | Mathematical Proof | 4 |
| COMP 570 | Statistics in Computing and Engineering | 4 |
| MATH 645 | Linear Algebra for Applications | 4 |
| MATH 755 | Probability with Applications | 4 |
| MATH 756 | Principles of Statistical Inference | 4 |
| Computing | ||
| COMP 424 | Applied Computing 1: Foundations of Programming | 4 |
| or CS 415 | Introduction to Computer Science I | |
| COMP 525 | Data Structures Fundamentals | 4 |
| or CS 416 | Introduction to Computer Science II | |
| COMP 625 | Data Structures and Algorithms | 4 |
| or CS 515 | Data Structures and Introduction to Algorithms | |
| CS 420 | Foundations of Programming for Digital Systems | 4 |
| CS 659 | Introduction to the Theory of Computation | 4 |
| COMP 740 & MATH 738 | Machine Learning Applications and Tools and Data Mining and Predictive Analytics | 8 |
| or COMP 740 & DATA 674 | Machine Learning Applications and Tools and Predictive and Prescriptive Analytics I | |
| or DATA 674 & DATA 675 | Predictive and Prescriptive Analytics I and Predictive and Prescriptive Analytics II | |
| CS 758 | Algorithms | 4 |
| COMP 720 | Database Systems and Technologies | 4 |
| Analytics & Data Science | ||
| DATA 557 | Introduction to Data Science and Analytics | 4 |
| English | ||
| ENGL 502 | Professional and Technical Writing | 4 |
| Analytics Course Capstone | 4 | |
| Capstone Project | ||
| Senior Project I and Senior Project II | ||
or CS 799 | Thesis | |
| Select Approved Minor 1 | ||
| Total Credits | 80 | |
- 1
Select an approved minor in consultation with the minor supervisor. Must be in a discipline to which Analytics and Data Science can be applied (examples include: Economics, Applied Mathematics) for the Data Science Option.
For additional information about the Analytics and Data Science: Data Science Option, contact program coordinator Jeremiah Johnson or the UNH Manchester Office of Admissions at (603) 641-4150.
This degree plan is a sample and does not reflect the impact of transfer credit or current course offerings. UNH Manchester undergraduate students will develop individual academic plans with their professional advisor during the first year at UNH.
Sample Course Sequence
| First Year | ||
|---|---|---|
| Fall | Credits | |
| MATH 425 | Calculus I | 4 |
| COMP 424 or CS 415 |
Applied Computing 1: Foundations of Programming or Introduction to Computer Science I |
4 |
| ENGL 401 | First-Year Writing | 4 |
| Discovery Course | 4 | |
| Credits | 16 | |
| Spring | ||
| MATH 426 | Calculus II | 4 |
| COMP 525 or CS 416 |
Data Structures Fundamentals or Introduction to Computer Science II |
4 |
| DATA 557 or CS 457 |
Introduction to Data Science and Analytics or Introduction to Data Science and Analytics |
4 |
| CS 420 | Foundations of Programming for Digital Systems | 4 |
| Credits | 16 | |
| Second Year | ||
| Fall | ||
| MATH 645 | Linear Algebra for Applications | 4 |
| MATH 531 | Mathematical Proof | 4 |
| COMP 625 or CS 515 |
Data Structures and Algorithms or Data Structures and Introduction to Algorithms |
4 |
| ENGL 502 | Professional and Technical Writing | 4 |
| Credits | 16 | |
| Spring | ||
| COMP 570 or MATH 644 |
Statistics in Computing and Engineering or Statistics for Engineers and Scientists |
4 |
| CS 659 | Introduction to the Theory of Computation | 4 |
| MATH 528 | Multidimensional Calculus | 4 |
| Discovery Course | 4 | |
| Credits | 16 | |
| Third Year | ||
| Fall | ||
| MATH 755 | Probability with Applications | 4 |
| MATH 738 | Data Mining and Predictive Analytics 1 | 4 |
| Minor Course | 4 | |
| Discovery Course | 4 | |
| Credits | 16 | |
| Spring | ||
| MATH 756 | Principles of Statistical Inference | 4 |
| CS 750 | Machine Learning 1 | 4 |
| CS 755 | Computer Vision | 4 |
| Discovery Course | 4 | |
| Credits | 16 | |
| Fourth Year | ||
| Fall | ||
| CS 758 | Algorithms | 4 |
| DATA #790 | Capstone Project | 4 |
| Minor Course | ||
| Discovery Course | ||
| Credits | 8 | |
| Spring | ||
| Minor Course | 4 | |
| Minor Course | 4 | |
| Minor Course | 4 | |
| Discovery Course | 4 | |
| Credits | 16 | |
| Total Credits | 120 | |
- 1
Either MATH 738 and CS 750, or DATA 674 and DATA 675, or DATA 674 and CS 750
- 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.