Data Science Major (B.S.)

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.

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 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).

Required Courses
Mathematics
MATH 425Calculus I4
MATH 426Calculus II4
MATH 528Multidimensional Calculus4
MATH 531Mathematical Proof4
MATH 539Introduction to Statistical Analysis4
or MATH 644 Statistics for Engineers and Scientists
MATH 645Linear Algebra for Applications4
or MATH 545 Introduction to Linear Algebra
MATH 755Probability with Applications4
MATH 756Principles of Statistical Inference4
Computer Science
CS 400Introduction to Computing2
CS 415Introduction to Computer Science I4
or CS 410P Introduction to Scientific Programming/Python
CS 416Introduction to Computer Science II4
CS 420Foundations of Programming for Digital Systems4
CS 457Introduction to Data Science and Analytics4
CS 515Data Structures and Introduction to Algorithms4
CS 659Introduction to the Theory of Computation4
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 758Algorithms4
IT 630Data Science and Big Data Analytics4
or CS 775 Database Systems
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) additional courses20
Total Credits98
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).

Instead of a minor, students may complete four (4) 600/700-level CS or MATH courses plus one (1) general elective. The additional general elective is required to meet the minimum 128 credits needed for graduation.

Sample Degree Plan

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.

Plan of Study Grid
First Year
FallCredits
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
 Credits18
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
 Credits16
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
 Credits16
Spring
MATH 539
or MATH 644
 Introduction to Statistical Analysis
or Statistics for Engineers and Scientists
 4
MATH 528 Multidimensional Calculus 4
Minor Elective I 4
Discovery 4
 Credits16
Third Year
Fall
CS 659 Introduction to the Theory of Computation 4
CS 674 Fundamentals of Statistical Learning I 4
Minor Elective II 4
Discovery 4
 Credits16
Spring
CS 758 Algorithms 4
CS 675
or CS 750
 Fundamentals of Statistical Learning II
or Machine Learning
 4
Minor Elective III 4
Discovery Course 4
 Credits16
Fourth Year
Fall
CS 791 Senior Project I 2
MATH 755 Probability with Applications 4
IT 630 Data Science and Big Data Analytics 4
Minor Elective IV 4
Discovery 4
 Credits18
Spring
CS 792 Senior Project II 2
MATH 756 Principles of Statistical Inference 4
Minor Elective V 4
Discovery 4
 Credits14
 Total Credits130

Program Learning Outcomes

  • 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.