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.

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.

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.

Degree Requirements

Minimum Credit Requirement: 128 credits
Minimum Residency Requirement: 32 credits must be taken at UNH
Minimum GPA: 2.0 required for conferral*
Core Curriculum Required: Discovery & Writing Program Requirements
Foreign Language Requirement: No

All Major, Option and Elective Requirements as indicated.
*Major GPA requirements as indicated.

Major Requirements

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.

Mathematics
MATH 425Calculus I4
MATH 426Calculus II4
MATH 528Multidimensional Calculus4
MATH 531Mathematical Proof4
COMP 570Statistics in Computing and Engineering4
MATH 645Linear Algebra for Applications4
MATH 755Probability with Applications4
MATH 756Principles of Statistical Inference4
Computing
COMP 424Applied Computing 1: Foundations of Programming4
or CS 415 Introduction to Computer Science I
COMP 525Data Structures Fundamentals4
or CS 416 Introduction to Computer Science II
COMP 625Data Structures and Algorithms4
or CS 515 Data Structures and Introduction to Algorithms
CS 420Foundations of Programming for Digital Systems4
CS 659Introduction to the Theory of Computation4
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 758Algorithms4
COMP 720Database Systems and Technologies4
Analytics & Data Science
DATA 557Introduction to Data Science and Analytics4
English
ENGL 502Professional and Technical Writing4
Analytics Course Capstone 4
DATA #790
Capstone Project
or CS 791
& CS 792
 Senior Project I
and Senior Project II
or CS 799
 Thesis
Select Approved Minor 1
Total Credits80
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.

 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

Plan of Study Grid
First Year
FallCredits
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
 Credits16
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
 Credits16
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
 Credits16
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
 Credits16
Third Year
Fall
MATH 755 Probability with Applications 4
MATH 738 Data Mining and Predictive Analytics 1 4
Minor Course 4
Discovery Course 4
 Credits16
Spring
MATH 756 Principles of Statistical Inference 4
CS 750 Machine Learning 1 4
CS 755 Computer Vision 4
Discovery Course 4
 Credits16
Fourth Year
Fall
CS 758 Algorithms 4
DATA #790 Capstone Project 4
Minor Course
Discovery Course
 Credits8
Spring
Minor Course 4
Minor Course 4
Minor Course 4
Discovery Course 4
 Credits16
 Total Credits120
1

Either MATH 738 and CS 750, or DATA 674 and DATA 675, or DATA 674 and CS 750.

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