Twin Cities campus

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Twin Cities Campus

Business Analytics M.S.

Information & Decision Sciences
Curtis L. Carlson School of Management
Link to a list of faculty for this program.
Contact Information
MBA & MS Programs Carlson School of Management University of Minnesota 321 19th Ave S, Suite 1-110 Minneapolis, MN 55455 Phone: 612-625-5555
Email: msba@umn.edu
Website: https://carlsonschool.umn.edu/degrees/master-science-in-business-analytics
  • Program Type: Master's
  • Requirements for this program are current for Fall 2024
  • Length of program in credits: 45
  • This program requires summer semesters for timely completion.
  • Degree: Master of Science
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
The MS in Business Analytics (MSBA) program provides a strong foundation in data analytics by bringing together a diverse body of knowledge from consumer behavior, risk management, operations research, optimization, information systems, computer science, applied statistics, and decision theory for the purpose of data-driven business decision making in both public and private sectors. Students who graduate from this 45-credit program will have the deep quantitative capabilities and technical expertise to create business and social value by extracting useful insights and applying them in a variety of career settings. The Business Analytics MS can be completed in one year of full-time study or in two years part-time.
Accreditation
This program is accredited by AACSB International. The M.S. program in Business Analytics is STEM designated.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
Applicants must have a bachelor's degree from an accredited college or university.
Other requirements to be completed before admission:
- Demonstrated competency in computer programming in at least one of the following computer programming languages is required: Python, R, C, C++, C#, VB, Java, or PHP. Academic transcripts, certificates from online courses, or work experience may be cited to meet this requirement. - At least one-semester college level calculus course is required. - Work experience is not required, but preferred.
Special Application Requirements:
Applicants must submit all application materials through the University's admissions system including: - Online application & application fee. - Transcripts from all colleges/universities previously attended. Non-English transcripts must be accompanied by an English translation. - A GMAT or GRE General Test that is not more than five years old, with an acceptable score. A GMAT/GRE waiver is available for qualified candidates. - For international students, an acceptable score on the Test of English as a Foreign Language (TOEFL) International Language Testing System (IELTS). - Two letters of recommendations need to be submitted through the online application. - A personal statement - submit separate short essay responses to included prompts (2 pages maximum). See website for details. - Current resume that includes job responsibilities and accomplishments in the online application. - Applicants may choose to submit an essay to comment on any item(s) in their application they consider worthy of further explanation. - Admissions interview (by invitation only). - Video essay. For details, visit - https://carlsonschool.umn.edu/degrees/master-science-in-business-analytics/applying-admission
Applicants must submit their test score(s) from the following:
  • GRE
  • GMAT
International applicants must submit score(s) from one of the following tests:
  • TOEFL
  • IELTS
Key to test abbreviations (GRE, GMAT, TOEFL, IELTS).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan C: Plan C requires 45 major credits and up to credits outside the major. There is no final exam. A capstone project is required.
Capstone Project: Students engage in an experiential learning application of the analytics methodologies, techniques, and tools learned throughout the program to a real-world problem. The final project consists of the development and presentation of results, interpretations, insights, and recommendations.
This program may not be completed with a minor.
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 2.80 is required for students to remain in good standing.
Some business/basic technical requirements can be waived for students with degrees in related business areas/computer science.
Business/Management Fundamentals (13 credits)
Take the following courses. Take MSBA 6355 for 1.5 credits.
MSBA 6111 - Business Essentials (3.0 cr)
MSBA 6121 - Introduction to Statistics for Data Scientists (3.0 cr)
MSBA 6131 - Introduction to Business Analytics in R (3.0 cr)
MSBA 6141 -  Ethics and Data Privacy (1.0 cr)
MSBA 6345 - Consultative Problem-Solving & Agile Management for Analytics Projects (1.5 cr)
MSBA 6355 - Building and Managing Teams (0.5 cr)
Elective (2 credits)
Select at least 2 elective credits in consultation with the advisor.
APEC 5831 - Food and Agribusiness Marketplace (2.0-3.0 cr)
BLAW 6158 - The study of laws affecting private business and publicly-traded companies. (2.0 cr)
ENTR 6036 - Managing the Growing Business (2.0 cr)
FINA 6123 - Financial Services Industry (2.0 cr)
FINA 6325 - Behavioral Finance (2.0 cr)
IDSC 6003 - Accounting and Information Systems (2.0 cr)
IDSC 6041 - Information Technology Management (2.0 cr)
IDSC 6051 - Information Technologies and Solutions (2.0 cr)
IDSC 6423 - Enterprise Systems (2.0 cr)
INS 6105 - Corporate Risk Management (2.0 cr)
INS 6101 - Employee Benefits (2.0 cr)
INS 6205 - Insurance Theory and Practice (2.0 cr)
MBA 6111 - Organizational Behavior (2.0 cr)
MBA 6141 - Managerial Economics (2.0 cr)
MBA 6235 - Managerial Accounting (2.0 cr)
MBA 6315 - The Ethical Environment of Business (2.0 cr)
MCOM 5515 - Persuasive Writing in Business (2.0 cr)
MCOM 5535 - Strategies and Skills for Managerial Presentations (2.0 cr)
MGMT 6004 - Negotiation Strategies (2.0 cr)
MGMT 6032 - Strategic Alliances (2.0 cr)
MGMT 6033 - Strategy Implementation (2.0 cr)
MGMT 6041 - Competing Globally (2.0 cr)
MGMT 6055 - Management of Innovation and Change (2.0 cr)
MGMT 6084 - Management of Teams (2.0 cr)
MGMT 6311 - Cross-Cultural Management: Developing Intercultural Compentence (2.0 cr)
MGMT 6465 - Leadership and Personal Development (2.0 cr)
MILI 6235 - Pharmaceutical Industry: Business and Policy (2.0 cr)
MILI 6589 - Medical Technology Evaluation and Market Research (2.0 cr)
MILI 6985 - The Health Care Marketplace (2.0 cr)
MILI 6991 - Anatomy and Physiology for Managers (2.0 cr)
MILI 6995 - Medical Industry Valuation Laboratory (2.0 cr)
MKTG 6052 - Marketing Analytics: Managerial Decisions (2.0 cr)
MKTG 6086 - Digital Marketing (2.0 cr)
MSBA 6461 - Advanced AI for Business Applications (2.0 cr)
Technical Fundamentals (9 credits)
Take the following courses:
MSBA 6311 - Programming for Data Science (3.0 cr)
MSBA 6321 - Data Management, Databases, and Data Warehousing (3.0 cr)
MSBA 6331 - Big Data Analytics (3.0 cr)
Specialty Courses (15 credits)
Take the following courses:
MSBA 6411 - Exploratory Data Analytics (3.0 cr)
MSBA 6421 - Predictive Analytics (3.0 cr)
MSBA 6431 - Advanced Issues in Business Analytics (3.0 cr)
MSBA 6441 - Causal Inference via Econometrics and Experimentation (3.0 cr)
MSBA 6451 - Optimization and Simulation for Decision Making (3.0 cr)
Capstone Experience (6 credits)
Take the following course:
MSBA 6511 - Business Analytics Experiential Learning (3.0-6.0 cr)
Joint- or Dual-degree Coursework:
The MS in Business Analytics Program offers the following dual degree program options: MS-Business Analytics/MBA (up to 12 credits in common allowed); MS-Business Analytics/MS-Finance (up to 12 credits in common allowed); MS-Business Analytics/MHRIR (up to 7.5 credits in common allowed). Students may take a total of 12 credits in common among the academic programs.
 
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MSBA 6111 - Business Essentials
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Summer
Introduction to fundamental concepts and applications in core business disciplines such as financial accounting, marketing, operations, and strategy, with an emphasis on their connection to business analytics. The course aims to increase students? business acumen and allows them to effectively partner with key functional areas of an organization.
MSBA 6121 - Introduction to Statistics for Data Scientists
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Summer
This course is designed to develop statistical thinking, i.e., understanding variation and using data to identify possible sources of variation. Specific techniques include basic descriptive and inferential procedures and regression modeling. The emphasis is on understanding such analysis for their relevance to decision making.
MSBA 6131 - Introduction to Business Analytics in R
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Summer
Introduction to key processes, building blocks, and use cases of business analytics through R, including data acquisition, engineering, visualization, basic concepts of exploratory and predictive analytics, and lifecycle of business analytics projects.
MSBA 6141 - Ethics and Data Privacy
Credits: 1.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Explore the moral, social, ethical, and legal ramifications of the choices made at the different stages of the data analysis pipeline, from data collection and storage to analysis and use. Students will learn the basics of ethical thinking in data science, understand the history of ethical dilemmas in scientific work, study issues of fairness, transparency, and algorithmic bias associated with machine learning, and explore the distinct challenges associated with ethics and privacy in modern data science.
MSBA 6345 - Consultative Problem-Solving & Agile Management for Analytics Projects
Credits: 1.5 [max 1.5]
Grading Basis: A-F only
Typically offered: Every Spring
Consultative problem-solving techniques, including using collaborative frameworks to bring strategic thinking skills to analytics projects. Project management skills with a focus on the Agile mindset and the implementation of Scrum practices using tools such as Jira and Confluence. Teams will apply these skills in real-time through the Business Analytics Experiential Learning Project which will be run in conjunction with this course. prereq: MSBA student
MSBA 6355 - Building and Managing Teams
Credits: 0.5 [max 1.5]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Examine individual, group and organizational aspects of team effectiveness; learn and practice basic skills central to team management; develop appreciation for team leadership function; learn the tools for effective team decision making and conflict management; develop general diagnostic skills for assessment of team issues within and across organizations and national boundaries.
APEC 5831 - Food and Agribusiness Marketplace
Credits: 2.0 -3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
This is a graduate student survey course of the industrial organization and current policy issues in the food and agribusiness marketplace. It represents a collaboration between the College of Food, Agricultural, and Natural Resource Sciences and the Carlson School of Management. The course uses short readings and speakers. A comprehensive look at all of the sectors in the food and agribusiness value chain is described. Topics include food policies (Farm Bills, food stamps, food labeling, and similar topics); environmental policies (water, invasive species, agriculture production and similar topics); and industrial organization issues (marketing and production contracts, overview of firm strategic orientation, distribution and similar topics). Readings, guest speakers, and presentations are used. prereq: graduate student
BLAW 6158 - The study of laws affecting private business and publicly-traded companies.
Credits: 2.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course highlights topics that are important to any business manager, with particular emphasis on areas of interest for those aspiring to high level executive/management positions with publicly-traded companies. General topics include: contracts, real estate law, the law of agency, employment law, certain discrimination laws (including Minnesota's fairly recent protections for women in the workplace), and forms of business entity. Public company subjects include: pros and cons of going public, the IPO process, federal securities laws and SEC regulations regarding public company reporting requirements, insider trading, the Sarbanes-Oxley Act of 2002 and its impact on corporate governance, trends in shareholder democracy rights and shareholder activism, and the role of boards and audit committees. Throughout the course, we will examine the impact of the Supreme Court on American business. prereq: MBA or Mgmt Sci MBA student
ENTR 6036 - Managing the Growing Business
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring & Summer
Challenges posed by rapid growth/change in independent startups. Infrastructure development, radical changes in strategy, continuous needs for substantial additional resources. Emphasizes analysis of factors accelerating/impeding growth and review/creation of growth strategies. Integration of concepts from strategy, operations, marketing, finance, and human resource management. prereq: MBA or Mgmt Sci MBA student
FINA 6123 - Financial Services Industry
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course gives an overview of the U.S. financial services industry, emphasizing the overall environment, key institutional details, and underlying economic functions. After introducing financial markets and institutions and their functions, we look at the biggest sectors of this industry (banking, insurance, securities dealing, money management, etc.) in more depth. We conclude with a discussion of the impact of "fintech" on this sector.
FINA 6325 - Behavioral Finance
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Psychology/realistic settings that guide/develop alternative theories of financial market. How behavioral finance complements traditional paradigm on investors' trading patterns, behavior of asset prices, corporate finance, various Wall Street institutions/practices. prereq: MBA or Mgmt Sci MBA student
IDSC 6003 - Accounting and Information Systems
Credits: 2.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Periodic Fall & Spring
IS/IT infrastructure assessment methods, technology solutions, management issues. Digital data sources. Systems design in accounting and financial reporting information systems. Internal control requirements of Sarbanes-Oxley Act of 2002. Experiential learning, hands-on use of accounting enterprise software other packages.
IDSC 6041 - Information Technology Management
Credits: 2.0 [max 2.0]
Course Equivalencies: IDSc 6040/MBA 6241
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Management of information systems, information technology (IT) in global organization. Strategic uses of IT. Alignment of IT, organizational strategy, internet/Web technologies, e-commerce customer services. Integration of e-business applications, interorganizational systems, systems implementation. Management of information as resource. Lecture, case analysis, classroom discussion. Prereq MBA student.
IDSC 6051 - Information Technologies and Solutions
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Current/emerging technologies in modern Net-enhanced organizations. Internet/Web technologies, including Internet fundamentals, Web communications, Web 2.0/social media, information security, cloud computing, IT-driven innovation, emerging IT trends.
IDSC 6423 - Enterprise Systems
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Enterprise Systems are the information core of diverse organizations and play a major role in their management and performance. This course provides the context of Enterprise Systems role in organization's journey of Digital Transformation. It examines Enterprise System's structural aspects such as governance, program & change management, sourcing, development (programming), testing, operations, and regulatory compliance. Business cases provide real world examples across these subjects and focus on specifics such as labor multi-sourcing and A/B testing strategies.
INS 6105 - Corporate Risk Management
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Theory applied to corporate risk management and insurance practices. Identification, measurement, and treatment of an organization.s financial risks integrated with its property, liability, workers compensation, and human resource risks. Selection and application of risk control and risk financing tools: risk retention, reduction and transfer, including insurance.
INS 6101 - Employee Benefits
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall
Design/administration of employee benefit plans/pension. Health insurance, disability plans. Salary reduction/deferred compensation programs. Multiple employer trusts. Alternative funding methods, including self-insurance. Ethical issues, legal liability, compliance.
INS 6205 - Insurance Theory and Practice
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Risk theory is applied to practices in health, liability, life, property, and workers compensation insurance. Insurance marketing, pricing, underwriting, and claims administration, with adverse selection and moral hazard effects. Policy issues of tort versus no-fault compensation systems. Self-insurance and integrated risk financing methods.
MBA 6111 - Organizational Behavior
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Organizational behavior offers a framework for the systematic study of how people behave in organizational settings and involves individual, group, and organizational characteristics that affect people and their behavior at work. In this course we consider how individual workers respond to their job and organization (attitudes and motivation), interpersonal processes and how to make them more effective (decision making, conflict management, teamwork), and the role organizational culture in shaping individual and group behavior. Topics come together as we consider how to effectively lead organizational change. Prior to Fall 2022 the course number was MBA 6110. Prior to Spring 2023 the course name was Leading Others.
MBA 6141 - Managerial Economics
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Introduction to some parts of microeconomics that are useful for managers, with attention to the circumstances that give rise to firm profitability. The first half of the course covers supply and demand, price elasticity, and market equilibrium. The second part of the course covers firms with differentiated products and market power, with particular focus on pricing models such as segmentation, bundling, and two part tariffs. Pricing models involve profit maximization and associated conceptual tools. The course touches on game theory and strategic interaction among small numbers of firms and ends with a discussion of market failure and the business opportunities that they sometimes create. The course also emphasizes links to other parts of the core business curriculum. The course makes extensive reference to statistical empirical examples. prereq: MBA or Mgmt Sci MBA student
MBA 6235 - Managerial Accounting
Credits: 2.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Cost systems introduced as potential sources of sustainable competitive advantage. Course focuses on designing cost systems to provide manager with accurate, relevant, and timely information. Taught as part of an integrated functional core. prereq: MBA or Mgmt Sci MBA student
MBA 6315 - The Ethical Environment of Business
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Understanding the ethical environment within which business and managers operate. Focus is on the ethical expectations surrounding organizational activities, firm responsibilities to shareholders and stakeholders, and providing a comprehensive framework for ethical decision-making by individuals. The goal of the class is two-fold. First, to help people in business find a voice and advance a point of view as they go forward with their career. Second, to prepare managers to successfully navigate and manage this critical component of a firm?s competitive environment. prereq: MBA student
MCOM 5515 - Persuasive Writing in Business
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Periodic Fall
Writing to motivate/affect change. Form/content. Techniques of persuasion. Producing polished text. Writing with power. prereq: MBA student
MCOM 5535 - Strategies and Skills for Managerial Presentations
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Periodic Fall
Delivering key messages with clarity/confidence, regardless of audience or setting. Maximizing impact as a speaker, seated/standing. Personal communication style and audience. Tailoring message. Handling questions/answers. Using audio/visual tools. Presenting as a team. prereq: MBA or Mgmt Science MBA student
MGMT 6004 - Negotiation Strategies
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
At its core, negotiation is the art and science of getting what you want in a world of innumerable interests, possibilities, and standards of fairness---a world in which we must often compete or cooperate with others to do anything from picking a restaurant to transforming markets. The objective of this course is to equip students with a simple, ready-to-use framework from which we can prepare for and engage in negotiations. Topics include interest-based bargaining, psychological biases, multiparty negotiations, and hard tactics. Regular cases and exercises reinforce our negotiation framework and provide students a safe forum to thoughtfully reflect on their experiences and improve. prereq: MBA or Mgmt Sci MBA student
MGMT 6032 - Strategic Alliances
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Periodic Fall & Spring
How inter-/intra-alliance rivalry influences global competitive landscape. How interplay of competitive/cooperative arrangements among firms invigorate intellectual/operational tasks. Designing/managing international strategy, organizational structure, and alliances. prereq: MBA or Mgmt Sci MBA student
MGMT 6033 - Strategy Implementation
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Periodic Fall & Spring
This course focuses on strategy execution at both the organizational and functional levels. Specific topics include the relationships between strategy formulation and execution, and between implementation and change. The course goes into depth on the systemic and structural problems that make most of these efforts difficult and often unsuccessful, along with various methods to minimize these problems. prereq: MBA or Mgmt Sci MBA student
MGMT 6041 - Competing Globally
Credits: 2.0 [max 2.0]
Course Equivalencies: Mgmt 6040/MGMT 6041
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Dealing with enormous complexity in competitive environment, in strategy, and in organizations. Focuses on strategic/organizational issues in managing across borders. prereq: MBA or Mgmt Sci MBA student
MGMT 6055 - Management of Innovation and Change
Credits: 2.0 [max 2.0]
Course Equivalencies: Mgmt 6050/Mgmt6055
Grading Basis: A-F only
Typically offered: Every Fall & Spring
How organizations innovate/change. Focuses on innovation in wide variety of new technologies, products, programs, and services. What paths likely to lead to success/failure. prereq: MBA or Mgmt Sci MBA student
MGMT 6084 - Management of Teams
Credits: 2.0 [max 2.0]
Course Equivalencies: HRIR 6484/Mgmt 6084
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Factors that influence performance and well-being of groups in organizations. Group dynamics, norms, culture, structure, leadership, decision-making, and problem-solving. Managing dynamics, learning, performance, and creativity of groups. Intergroup relations, incentives, and effect of environment.
MGMT 6311 - Cross-Cultural Management: Developing Intercultural Compentence
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
The emphasis of this course is on people-related (i.e., psychological and behavioral) issues that arise when managing across cultures. Through the use of cases and interactive experiential activities, this course will develop your intellectual ability to critically examine, analyze, and deal with cross-cultural problems in business contexts, while also cultivating a tolerance for ambiguity that is necessary in the global workplace. The combination of materials and experiences will allow you to evaluate your cross-cultural savvy, understand and appreciate the nuances of cultural identities and the impact these have on work relationships, and create a plan to increase your intercultural competence. Prior to Spring 2023 course number was: MBA 6310.
MGMT 6465 - Leadership and Personal Development
Credits: 2.0 [max 2.0]
Course Equivalencies: HRIR 6025/Mgmt 6465
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Understanding effective leadership. Identifying personal leadership strengths/vulnerabilities through feedback. Developing leadership skills through practice as informed by theory/evidence. Exercises, role play. Creating customized leadership development plan. prereq: CSOM Grad student or dept consent
MILI 6235 - Pharmaceutical Industry: Business and Policy
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Business/policy issues specific to pharmaceutical industry. Interdisciplinary perspectives, active involvement by industry leaders.
MILI 6589 - Medical Technology Evaluation and Market Research
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course aims to provide knowledge of the skills, data, and methodology required to critically evaluate new medical technologies in order to meet financial investment as well as regulatory compliance objectives, such as FDA approval. The course is designed to provide an introduction to the analytic tool kit needed to critically evaluate new medical technology, such as cost-benefit analysis, cost effectiveness analysis as well as other decision-analytic models and markov-models.
MILI 6985 - The Health Care Marketplace
Credits: 2.0 [max 2.0]
Course Equivalencies: MILI 5990/6990/3585/5585
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Survey of trillion dollar medical industry. Physician/hospital services, insurance, pharmaceuticals, medical devices, information technology. Scale, interactions, inter-relationships, market opportunities, barriers. prereq: MBA student
MILI 6991 - Anatomy and Physiology for Managers
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Overview of medical vocabulary/physiology of major body systems. Understanding current clinical practice. Market opportunities of major body systems, Medical technology innovation.
MILI 6995 - Medical Industry Valuation Laboratory
Credits: 2.0 [max 6.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Interdisciplinary student teams create rapid production market analysis of promising medical technologies/services to determine potential for success in market. Exposure to University innovations, venture firms, inventors. prereq: Grad student
MKTG 6052 - Marketing Analytics: Managerial Decisions
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Modern marketers use data to drive decisions. This course teaches students a suite of statistics analytic tools to make strategic decisions. Focusing on learning how to apply specific analytic tools to different managerial challenges, students will learn how to leverage data to perform market analyses, segmentation and targeting, customer value assessment, brand management, new product development, among other tasks. Students will be able to apply the learned skills to their work immediately to produce data-driven insights and develop strategic recommendations. The course is also helpful for students who are interested in STEM to improve their stats modeling and other relevant skills.
MKTG 6086 - Digital Marketing
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Periodic Fall & Spring
Marketing practices have dramatically shifted with the rise of social media and the proliferation of devices, platforms, and applications. This rapidly changing environment presents new opportunities and challenges for marketers. Through a combination of case studies, best practice examples, current news items, and assignments, students learn how the elements of a digital strategy work together with traditional media to attract prospective customers. Specifically, students learn best practices for social media marketing, content marketing, organic and paid search, search engine optimization, e-mail marketing, landing pages and display advertising. Students discuss strategies for reputation management in a world where information is disseminated virally and discover how social media monitoring and data analysis can be used to improve marketing and product development activities. The importance of establishing digital marketing goals and analytics is covered as well as how to measure return on investment for digital activities. Additional focus on analytics through certification assignments, in google analytics and ad search. Exploration of return on marketing measurement and evaluation of digital tactics.
MSBA 6461 - Advanced AI for Business Applications
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course covers some advanced topics in machine learning and artificial intelligence for solving analytics problems and building business applications. Topics include but are not limited to: reinforcement learning and recent advances on natural language processing. Students are introduced to the basic concepts of reinforcement learning such as Markov decision process, bandits, and regret minimization, and are trained to build simple and practical reinforcement learning systems. The course also discusses some recent progress in natural language processing / understanding, such as representation learning, sequential models, and transformer models, as well as their business applications.
MSBA 6311 - Programming for Data Science
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
According to recent industry surveys, Python is one of the most popular tools used by organizations data analysis. We will explore the emerging popularity of Python for tasks such as general purpose computing, data analysis, website scraping, and data visualization. You will first learn the basics of the Python language. Participants will then learn how to apply functionality from powerful and popular data science-focused libraries. In addition, we will learn advanced programming techniques such as lambda functions and closures. We will spend most of our class time completing practical hands-on exercises.
MSBA 6321 - Data Management, Databases, and Data Warehousing
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Fundamentals of database modeling/design, normalization. Extract, transform, load. Data cubes/setting up data warehouse. Data pre-processing, quality, integration/stewardship issues. Advances in database/storage technologies.
MSBA 6331 - Big Data Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Exploring big data infrastructure and ecosystem, ingesting and managing big data, analytics with big data; Hadoop, MapReduce, Hive, Spark, scalable machine Learning, scalable real-time streaming analytics, NoSQL, cloud computing, and other recent developments in big data.
MSBA 6411 - Exploratory Data Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Fundamentals of exploratory business analytics. Solving real-world business problems using appropriate data analysis techniques and effective technical/managerial communication. Foundational methods allow for the detection of relationships and patterns in structured and unstructured data through clustering, dimensionality reduction, probabilistic graphical models, anomaly detection, and deep neural networks.
MSBA 6421 - Predictive Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Fundamentals of predictive modeling and machine learning, assessing the performance of predictive models: logistic regression, decision trees, naïve Bayesian classifiers, support vector machine, ensemble learning, deep neural network, and their applications in structured and unstructured data.
MSBA 6431 - Advanced Issues in Business Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Spring
Analysis of time series data, interpretation and forecasting; fundamentals of network analysis, mining digital media and social networks, community detection and friend recommendation; personalization technologies, and recommender systems.
MSBA 6441 - Causal Inference via Econometrics and Experimentation
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Controlled experiments in business settings, experiment design, A/B testing. Specialized statistical methodologies. Fundamentals of econometrics, instrument variable regression, propensity score matching.
MSBA 6451 - Optimization and Simulation for Decision Making
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Spring
Fundamentals of decision analysis, linear optimization, mixed integer linear programming, Bayesian inference, Monte Carlo simulation, and decision technologies.
MSBA 6511 - Business Analytics Experiential Learning
Credits: 3.0 -6.0 [max 6.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course involves hands-on application of the analytics methodologies, techniques, and tools learned throughout the program to a real-world problem (such as consulting for a real-world business client in the area of marketing, strategy, operation/supply chain, information technology, finance, accounting, or human resources) as well as the development and presentation of results, interpretations, insights, and recommendations.
MSBA 6121 - Introduction to Statistics for Data Scientists
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Summer
This course is designed to develop statistical thinking, i.e., understanding variation and using data to identify possible sources of variation. Specific techniques include basic descriptive and inferential procedures and regression modeling. The emphasis is on understanding such analysis for their relevance to decision making.
MSBA 6250 - Analytics for Competitive Advantage
Credits: 3.0 [max 1.5]
Grading Basis: A-F only
Typically offered: Every Summer
Case/discussion-based introduction to variety of analytics-related issues/examples in business. Business value, impact, benefits/limitations, as well as ethical, legal, privacy issues. Use of case studies, examples, guest speakers.
MSBA 6311 - Programming for Data Science
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
According to recent industry surveys, Python is one of the most popular tools used by organizations data analysis. We will explore the emerging popularity of Python for tasks such as general purpose computing, data analysis, website scraping, and data visualization. You will first learn the basics of the Python language. Participants will then learn how to apply functionality from powerful and popular data science-focused libraries. In addition, we will learn advanced programming techniques such as lambda functions and closures. We will spend most of our class time completing practical hands-on exercises.
MSBA 6321 - Data Management, Databases, and Data Warehousing
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Fundamentals of database modeling/design, normalization. Extract, transform, load. Data cubes/setting up data warehouse. Data pre-processing, quality, integration/stewardship issues. Advances in database/storage technologies.
MSBA 6331 - Big Data Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Exploring big data infrastructure and ecosystem, ingesting and managing big data, analytics with big data; Hadoop, MapReduce, Hive, Spark, scalable machine Learning, scalable real-time streaming analytics, NoSQL, cloud computing, and other recent developments in big data.
IDSC 6490 - Advanced Topics in MIS
Credits: 2.0 [max 10.0]
Grading Basis: A-F only
Typically offered: Periodic Fall & Spring
Discussion and analysis of topics and developments in managing information systems.
MSBA 6411 - Exploratory Data Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Fundamentals of exploratory business analytics. Solving real-world business problems using appropriate data analysis techniques and effective technical/managerial communication. Foundational methods allow for the detection of relationships and patterns in structured and unstructured data through clustering, dimensionality reduction, probabilistic graphical models, anomaly detection, and deep neural networks.
MSBA 6421 - Predictive Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Fundamentals of predictive modeling and machine learning, assessing the performance of predictive models: logistic regression, decision trees, naïve Bayesian classifiers, support vector machine, ensemble learning, deep neural network, and their applications in structured and unstructured data.
MSBA 6431 - Advanced Issues in Business Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Spring
Analysis of time series data, interpretation and forecasting; fundamentals of network analysis, mining digital media and social networks, community detection and friend recommendation; personalization technologies, and recommender systems.
MSBA 6441 - Causal Inference via Econometrics and Experimentation
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Controlled experiments in business settings, experiment design, A/B testing. Specialized statistical methodologies. Fundamentals of econometrics, instrument variable regression, propensity score matching.
MSBA 6451 - Optimization and Simulation for Decision Making
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Spring
Fundamentals of decision analysis, linear optimization, mixed integer linear programming, Bayesian inference, Monte Carlo simulation, and decision technologies.
MSBA 6515 - Capstone Project in Analytics
Credits: 0.0 -3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Spring
Hands-on, integrative application of analytics methodologies, techniques, and tools learned throughout the program in the context of a specific analytics problem. Experience with the entire data analytics cycle, starting from business and data understanding as well as data cleaning and integration and ending with the development and presentation of results, interpretations, insights, and recommendations.