Analytics and Data Science Major: Data Science Option (B.S.)
https://ceps.unh.edu/computer-science/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
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.
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 | |
or COMP 570 | Statistics in Computing and Engineering | |
MATH 645 | Linear Algebra for Applications | 4 |
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 | |
or COMP 424 | Applied Computing 1: Foundations of Programming | |
CS 416 | Introduction to Computer Science II | 4 |
or COMP 525 | Data Structures Fundamentals | |
CS 420 | Foundations of Programming for Digital Systems | 4 |
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 | |
CS 659 | Introduction to the Theory of Computation | 4 |
CS 750 & MATH 738 | Machine Learning and Data Mining and Predictive Analytics | 8 |
or DATA 674 & DATA #675 | Predictive and Prescriptive Analytics I and Predictive and Prescriptive Analytics II | |
or DATA 674 & CS 750 | Predictive and Prescriptive Analytics I and Machine Learning | |
CS 758 | Algorithms | 4 |
CS 775 | Database Systems | 4 |
English | ||
ENGL 502 | Professional and Technical Writing | 4 |
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) courses for the minor | 20 | |
Total Credits | 102 |
- ^{ 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).
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 |
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 |
ENGL 502 | Professional and Technical Writing | 4 |
Discovery Lab | 4 | |
Credits | 16 | |
Third Year | ||
Fall | ||
CS 659 | Introduction to the Theory of Computation | 4 |
CS 750 | Machine Learning | 4 |
DATA 674 | Predictive and Prescriptive Analytics I | 4 |
Discovery Course | 4 | |
Credits | 16 | |
Spring | ||
CS 758 | Algorithms | 4 |
Minor Elective I | 4 | |
Minor Elective II | 4 | |
Discovery Course | 4 | |
Credits | 16 | |
Fourth Year | ||
Fall | ||
CS 791 | Senior Project I | 2 |
MATH 755 | Probability with Applications | 4 |
Discovery Course | 4 | |
Minor Elective III | 4 | |
Minor Elective IV | 4 | |
Credits | 18 | |
Spring | ||
CS 792 | Senior Project II | 2 |
CS 775 | Database Systems | 4 |
MATH 756 | Principles of Statistical Inference | 4 |
Minor Elective V | 4 | |
Credits | 14 | |
Total Credits | 130 |
- 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.