Decision Sciences (DS)
Visit the Course Schedule Search website to find out when courses will be offered during the academic year.
Read more about the courses within this subject prefix in the descriptions provided below.
DS 444 - Meaning of Entrepreneurship
Credits: 4
This course explores the idea and ideals of entrepreneurship, the creating of value through individual initiative, creativity and innovation. The idea of entrepreneurship is of significant relevance in the highly dynamic and competitive 21st century global economy. It is an idea that is important for students to understand and to critically consider and apply. Encourages the development of multiple views of entrepreneurship, and uses a broad, not just business, approach to the study as it engages students in the subject matter. Open to all majors.
Attributes: Environment,TechSociety(Disc); Inquiry (Discovery); Writing Intensive Course
Grade Mode: Letter Grading
DS 520 - Topics in Entrepreneurship/Decision Sciences
Credits: 4
Special topics in entrepreneurship, information systems and business analytics. Vary by semester.
Repeat Rule: May be repeated for a maximum of 8 credits.
Grade Mode: Letter Grading
DS 550 - Introduction to Angel and Venture Capital Investing
Credits: 2
This course serves as the foundational course for students entering the Mel Rines Student Angel Investment Fund as Associates. This introductory course provides students with essential knowledge and skills in private equity, angel investing, and venture capital, preparing them for hands-on participation in the Rines Angel Fund in DS 650. The course offers a comprehensive overview of the private equity landscape, with particular emphasis on early-stage investing from both the entrepreneur's and investor's perspectives. Students will learn the fundamental processes involved in evaluating startup companies, conducting preliminary research, and supporting investment analysis activities.
Repeat Rule: May be repeated for a maximum of 8 credits. May be repeated up to 4 times.
Grade Mode: Credit/Fail Grading
DS 620 - Topics in Decision Sciences
Credits: 1-4
Special topics, vary by semester.
Repeat Rule: May be repeated for a maximum of 12 credits.
Grade Mode: Letter Grading
DS 650 - The Mel Rines Student Angel Investment Fund
Credits: 2
The Mel Rines Student Angel Investment Fund is a cross-disciplinary, undergraduate, student-managed private equity fund. The Fund allows students to learn angel and venture capital investment strategies through the first-hand experience of investing in start-up companies. Students evaluate entrepreneur pitches, conduct due diligence on potential investments, work with angel partners, and present to an investment committee their recommendations to invest capital. Students interested in joining the Fund must submit an application and undergo an interview process. Students in good standing may retake the course.
Repeat Rule: May be repeated for a maximum of 12 credits.
Grade Mode: Credit/Fail Grading
DS 652 - Artifex
Credits: 2
Artifex aims is to equip its members with the essential skills of a data scientist. The course delivery is a mix of lectures and project-based learning. Lectures and course content are tailored to the business analytics project(s) we are working on in any given semester. Artifex is also an active and growing student club. As such, Artifex is a great opportunity to network with other students and professionals who are passionate about using data to improve the way businesses work.
Repeat Rule: May be repeated for a maximum of 8 credits.
Grade Mode: Credit/Fail Grading
DS 654 - Sports Analytics Lab
Credits: 2
This course will explore "real-world" applications of data and insights to UNH athletics and outside sports organizations. You will learn terms commonly used in sports analytics and begin to understand some of the complex decisions that an athletic organization makes with data, because you will be helping them do it! This course is group project oriented with a few whole class meetings to share best practices and outcomes. Students will be split into teams of 3-5and be assigned to a sports client.
Repeat Rule: May be repeated for a maximum of 12 credits. May be repeated up to 6 times.
Grade Mode: Credit/Fail Grading
DS 662 - Programming for Business
Credits: 4
Introduces students to programming concepts. Covers fundamentals including functions, variable types, conditionals, and data structures. Students apply these concepts to a variety of business analytics problems including data collection, wrangling, reshaping, summarizing , and visualization.
Prerequisite(s): ADMN 410 with a minimum grade of C-.
Equivalent(s): DS 562
Grade Mode: Letter Grading
DS 671 - Data Visualization and Prescriptive Analytics
Credits: 4
The course focuses on Descriptive and Prescriptive Analytics. Students gain modeling and data analysis and visualization skills necessary to address a wide variety of business problems. In Descriptive Analytics, students learn principles of data visualization, data cleanup and wrangling, advanced data analysis and visualization tools, and dashboard design. In Prescriptive Analytics, students learn advanced spreadsheet modeling/programming, formulating and solving a variety of optimization problems, and performing sensitivity analysis.
Prerequisite(s): (ADMN 410 with a minimum grade of C- or CS 415 with a minimum grade of C-) and (ADMN 510 with a minimum grade of C- or MATH 539 with a minimum grade of C- or MATH 540 with a minimum grade of C-).
Equivalent(s): DS 766
Mutual Exclusion: No credit for students who have taken SC 671.
Grade Mode: Letter Grading
DS 673 - Database Management
Credits: 4
Provides students with the skills necessary to understand the database environment of the firm. Topics include data models, normalization, SQL, data warehouses, and nosQL databases. Students learn to design and implement moderately complex relational databases in multi-user, client/server environments.
Prerequisite(s): ADMN 410 with a minimum grade of C-.
Equivalent(s): DS 773
Grade Mode: Letter Grading
DS 720 - Topics in Decision Sciences II
Credits: 4
Introduces students to commonly used predictive analytics techniques and necessary programming with focus on regression analysis and model building. The course coverage is supported with real data applications and illustrations. The topics include linear and non-linear regression model building/selection, residual analysis, search algorithms, generalized linear models/classification, and clustering algorithms.
Repeat Rule: May be repeated for a maximum of 8 credits.
Grade Mode: Letter Grading
DS 741 - Startup Analytics
Credits: 4
This course introduces students to data analytics for startup capital management, with a special focus on leveraging AI and machine learning (ML) to optimize financial strategies, funding decisions, and investor relations. Through a hands-on approach, students will learn how to use data and machine learning techniques to forecast capital needs, evaluate funding sources, track startup performance, and optimize investor interactions. The course blends fundamental concepts of startup capital management with modern data-driven tools to help students make informed, data-centric decisions at each stage of startup growth.
Prerequisite(s): ADMN 570 with a minimum grade of C-.
Grade Mode: Letter Grading
DS 742 - Internship in Entrepreneurial and Management Practice
Credits: 4
Involves working for leading companies and dynamic entrepreneurs, as well as classroom instruction. The priority experiential, real-world, and real-time learning in the high-growth environment of entrepreneurial ventures. Focus on several topic areas, including venture capital.
Grade Mode: Letter Grading
DS 743 - Venture Scaling Strategies
Credits: 4
"Venture Scaling Strategies" is an entrepreneurship course designed to equip students with the essential tools and frameworks to scale a business successfully. Based on Verne Harnish's book Scaling Up, this course dives into the critical areas of People, Strategy, Execution, and Cash to help students develop comprehensive scaling strategies. Through practical applications, case studies, and interactive discussions, students will learn how to navigate the complexities of growth, create effective strategic plans, manage financial resources, build high-performing teams, and implement operational systems to support expansion. Ideal for aspiring entrepreneurs and business leaders, this course provides the roadmap to take ventures to the next level. The course will include a practical, hands-on, engagement project.
Grade Mode: Letter Grading
DS 772 - Predictive Analytics and Modeling
Credits: 4
The course introduces students to commonly used predictive analytics methods and necessary programming with a focus on regression analysis, classification, and model building. The course coverage is supported using real data applications and illustrations. The topics include linear and non-linear regression model building/selection, residual analysis, search algorithms, generalized linear models/classification, and applied machine learning methods for business use.
Prerequisite(s): ADMN 510 with a minimum grade of C- or MATH 539 with a minimum grade of C- or MATH 540 with a minimum grade of C-.
Grade Mode: Letter Grading
DS 774 - AI and Emerging Technologies in Business
Credits: 4
This course immerses students in the intersecting realms of technology and business. Students will explore key domains such as Artificial Intelligence, Cybersecurity, Global e-Business, Application Design, and Enterprise Systems, engaging in a hands-on, collaborative curriculum. Students will develop a strategic perspective on using IT innovations to drive business value, tackle real-world challenges, and build in-demand skills for dynamic technology careers.
Prerequisite(s): ADMN 410 with a minimum grade of C-.
Grade Mode: Letter Grading
View Course Learning Outcomes
- Identify how information systems, artificial intelligence, and emerging technologies are used to create and sustain competitive advantage across business functions.
- Explain the role of data, information, and AI-enabled systems in organizational decision-making, and anticipate how these technologies can support functional and managerial responsibilities.
- Analyze the managerial, technical, and organizational challenges associated with managing data, information, knowledge, and AI-driven systems, along with common approaches used to address them.
- Describe how artificial intelligence and other emerging technologies are applied in areas such as operations, marketing, finance, cybersecurity, and strategy, with attention to practical use cases and limitations.
DS 775 - Corporate Project Experience
Credits: 4
Provides real-life experience in organizations. Work in groups on information systems and/or business analytics projects identified by sponsoring organizations. Integrate concepts and skills learned in prior business, analytics, and information systems courses. Learn project management concepts, work with project management tools, and use presentation techniques. Two ISBA Electives required prior to taking this course.
Attributes: Writing Intensive Course
Prerequisite(s): DS 673 with a minimum grade of C-.
Grade Mode: Letter Grading
DS 799H - Honors Thesis in Decision Sciences
Credits: 4
Supervised research leading to the completion of an honors thesis or project in decision sciences; required for graduation from the honors program in business administration.
Attributes: Honors course; Writing Intensive Course
Repeat Rule: May be repeated for a maximum of 8 credits.
Grade Mode: Letter Grading