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
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 2019
  • 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. Dual MBA-MSBA Degree The Carlson School of Management offers a dual MBA-MSBA degree option. The MBA curriculum provides a rigorous business education while MSBA courses teach students how to collect and analyze data for business value. The combination of the two programs prepares graduates to identify business opportunities and advantages using data-derived insights. The MBA-MSBA dual degree can be completed in 2.5 years and culminates in the completion of two graduate degree programs.
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 proficiency in computer programming is required. The following programming languages satisfy the requirement: Python, R, C, C++, C#, VB, Java, Pascal, and Fortran. - Applicants must have completed at least one semester college-level Calculus course with a grade of "C" or better (or grade equivalent). - Work experience is not required, but preferred.
Special Application Requirements:
MSBA Application Requirements: Applicants must submit all application materials through the University's admissions system. Application materials include: - A GMAT or GRE General Test that is not more than five years old, with an acceptable score. PT MSBA applicants: 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). - Three letters of recommendations need to be submitted through the online application. - A personal statement of career goals, and objectives for pursuing a Business Analytics M.S. degree. The personal statement questions are the following: Briefly describe your short-term and long-term career goals. Why are you choosing to pursue a Business Analytics M.S. degree at this time in your career, and what are you hoping to accomplish by doing so? Why are you interested in pursuing a Business Analytics M.S. degree at the Carlson School of Management? What do you feel makes you a strong candidate for the program? How will you contribute to the Business Analytics M.S. Program overall? Applicants must submit a 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. - Applicants may be required to complete an admissions interview, which are by invitation only. MSBA/MBA Dual Degree Application Requirements: Students must be admitted to the MSBA and MBA programs separately. Students will take either the GRE or the GMAT as part of this process, and follow all other admissions criteria set by either program.
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 will 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 will consist 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 (15 credits)
MBA 6031 - Financial Accounting (3.0 cr)
MBA 6211 - Marketing Management (3.0 cr)
MSBA 6250 - Analytics for Competitive Advantage (3.0 cr)
MSBA 6121 - Introduction to Statistics for Data Scientists (3.0 cr)
MSBA 6345 - Consultative Problem-Solving & Agile Management for Analytics Projects (1.5 cr)
MSBA 6355 - Building and Managing Teams (0.5 cr)
Technical Fundamentals (9 credits)
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)
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)
MSBA 6511 - Business Analytics Experiential Learning (3.0-6.0 cr)
 
More program views..
View college catalog(s):
· Curtis L. Carlson School of Management

View future requirement(s):
· Fall 2023
· Fall 2022
· Fall 2020


View checkpoint chart:
· Business Analytics M.S.
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MBA 6031 - Financial Accounting
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Basic principles of financial accounting, involving the consecution/interpretation of corporate financial statements. prereq: MBA or Mgmt Sci MBA Student
MBA 6211 - Marketing Management
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Management of the marketing function; understanding the basic foundational marketing concepts and skills in strategy development and planning of operational and strategic levels pertaining to product offering decisions, distribution channels, pricing and communication. prereq: MBA student
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 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 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.
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 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 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 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.
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 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.