Analytics and Data Science Major: Analytics Option (B.S.) Manchester

https://manchester.unh.edu/program/bs/analytics-data-science-major-analytics-option

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 at UNH. The option in Analytics places its emphasis on applications of data science in industry.

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. 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, contact program coordinator Jeremiah Johnson or the UNH Manchester Office of Admissions, (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, completing all of the 21 required courses in the major as listed below, including the capstone course, the internship preparedness course, and an internship. 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).

Mathematics
MATH 425Calculus I4
MATH 426Calculus II4
MATH 545Introduction to Linear Algebra4
or MATH 645 Linear Algebra for Applications
MATH 739Applied Regression Analysis4
Computing
COMP 424Applied Computing 1: Foundations of Programming4
or CS 415 Introduction to Computer Science I
COMP 430Systems Fundamentals4
COMP 520Database Design and Development4
or IT 505 Integrative Programming
COMP 525Data Structures Fundamentals4
or CS 416 Introduction to Computer Science II
COMP 570Statistics in Computing and Engineering4
or MATH 539 Introduction to Statistical Analysis
or MATH 644 Statistics for Engineers and Scientists
COMP 625Data Structures and Algorithms4
or CS 515 Data Structures and Introduction to Algorithms
Business 112
Analytics & DATA Courses
DATA 557Introduction to Data Science and Analytics4
or CS 457 Introduction to Data Science and Analytics
DATA 674Predictive and Prescriptive Analytics I4
DATA #675Predictive and Prescriptive Analytics II4
DATA 757Mining Massive Datasets4
or COMP 721 Big Data for Data Engineers
Project and Professional Practice
DATA 690Internship Experience1-4
DATA 790Capstone Project4
or CS 791 Senior Project I
or CS 792 Senior Project II
Other
ENGL 502Professional and Technical Writing4
UMST 582Internship and Career Planning Seminar1
Total Credits78-81
1

In consultation with your advisor, select:
1 course (4 credits) in Introduction to Business
1 course (4 credits) in Organizational Behavior
1 course (4 credits) in Organizational Leadership

Sample Course Sequence

Plan of Study Grid
First Year
FallCredits
COMP 424
Applied Computing 1: Foundations of Programming
or Introduction to Computer Science I
4
ENGL 401 First-Year Writing 4
MATH 425 Calculus I 4
Discovery Course 4
 Credits16
Spring
BUS A 1 4
COMP 525
Data Structures Fundamentals
or Introduction to Computer Science II
4
DATA 557
Introduction to Data Science and Analytics
or Introduction to Data Science and Analytics
4
MATH 426 Calculus II 4
 Credits16
Second Year
Fall
COMP 625
Data Structures and Algorithms
or Data Structures and Introduction to Algorithms
4
MATH 645 Linear Algebra for Applications 4
Discovery Course 4
Elective 2 4
 Credits16
Spring
COMP 430 Systems Fundamentals 4
COMP 520
Database Design and Development
or Integrative Programming
4
COMP 570
Statistics in Computing and Engineering
or Introduction to Statistical Analysis
or Statistics for Engineers and Scientists
4
Discovery Course 4
 Credits16
Third Year
Fall
BUS B 1 4
DATA 674 Predictive and Prescriptive Analytics I 4
MATH 739 Applied Regression Analysis 4
Discovery Course 4
 Credits16
Spring
DATA #675
Predictive and Prescriptive Analytics II
or Big Data for Data Engineers
4
ENGL 502 Professional and Technical Writing 4
UMST 582 Internship and Career Planning Seminar 1
Discovery Course 4
Discovery Course 4
 Credits17
Fourth Year
Fall
BUS C 1 4
DATA 757 Mining Massive Datasets 4
Discovery Course 4
Elective 4
 Credits16
Spring
DATA 790
Capstone Project
or Senior Project I
or Senior Project II
4
Discovery Course 4
Elective 4
Elective 4
 Credits16
 Total Credits129
1

In consultation with your advisor, select: Introduction to Business, Organizational Behavior, or Organizational Leadership. 

2

MATH 531 Mathematical Proof strongly encouraged

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