Academics

Format

Students can complete the program in-person, online, or schedule a mix of on campus and online courses.
Adjustments can be made from semester to semester.

Schedule

To accommodate busy professionals, our classes are primarily offered in the evening and online. You can complete your degree in just four semesters (16 months), with convenient spring and fall start options.

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    Tuesday, April 28 | 

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Curriculum

Our business analytics curriculum provides students with the skills and knowledge to navigate the intersecting landscapes of business and data analytics. The hands-on coursework integrates concepts from marketing, operations, and information systems — equipping students with diverse perspectives and practical experience needed to succeed in a competitive job market.

Our 30 credit program features a lockstep curriculum, where every student takes the same courses in the same order. This cohort-based approach allows students to forge deeper connections with classmates and follow a predictable path to graduation where each semester builds upon the previous one.

Whether you start in the fall or the spring, you’ll take the same courses, but the order will differ to align with availability.

First Semester Courses

Course Number: MKTG 6487
Credits: 1.5

This course introduces students to the main elements of business analytics. The domains include framing the business problem, framing the analytics problem, managing data, selecting the methodology, building the model, deployment and life cycle management. Students will learn and practice the domains using small projects.

Course Number: IS 6420
Credits: 3

Advanced topics in database theory and design, including hands-on development of a working database system. Topics covered include the relational database model, foundations in relational algebra, design techniques, SQL, distributed databases, multimedia databases, and knowledge bases.

Course Number: IS 6489
Credits: 3

This is a graduate level course in statistics, with an emphasis on developing predictive models using an open source statistical programming language. The engaged student should expect to develop foundational skills for data analysis. Topics covered will include some or all of the following: descriptive statistics, nonparametric regression, probability distributions, linear and logistic regression, tree-based methods, model assumptions and model checking, cross-validation, simulation, resampling, visualization, and reproducible research.

Course Number: IS 6491
Credits: 1.5

Data Visualization is the graphical representation of information. Data Visualization and related technologies create value within organizations by providing insight from complex sets of data by communicating key aspects therefrom. This course focuses on how to increase the likelihood of action based on insights from data by telling stories with data that leverage effective Data Visualizations. The course includes a mix of theory and hands-on application using contemporary processes and Data Visualization technologies.

Second Semester Courses

Course Number: MKTG 6601
Credits: 1.5

This is a graduate-level course on causal inference to support business decisions. The course concentrates on how to design, size, and analyze simple but effective experiments.

Course Number: IS 6493
Credits: 3

Successful corporations can utilize data science techniques to help drive business decision making by analyzing datasets of varying sizes. In this course, a hands-on practitioner’s approach is taken to learning the fundamental knowledge, techniques and tools required for leading data science teams and analyzing big data. This course will utilize popular open source technologies and libraries in use today to learn how to collect, pre-process and visualize data, as well as build and test models for inference and prediction. We will examine the unique challenges posed by big data and complex models, and learn how to address them using distributed computing frameworks such as Dask, Hadoop and Spark. The course is taught in Python, and will offer a bootcamp in the first few weeks to help everyone get comfortable with the language.

Course Number: MKTG 6640
Credits: 3

The web is replete with unstructured data in the form of emails, tweets, blogs, customer reviews, and so on. This course introduces the tools and concepts that allow businesses to analyze textual data and shows how these methods can be used to make better business decisions in online and offline contexts. The course focuses on different analytical techniques and algorithms including preprocessing, text classification, text clustering, topic modeling, document summarization, and sentiment analysis. These methods are applied to different areas of business including marketing, human resource management, business law, accounting, and finance.

Course Number: IS 6812
Credit: 1.5

Students prepare for their capstone project through targeted career development and practice projects in business analytics. Students will leave this course prepared to complete a real-world analytics project for a business or nonprofit organization.

Third Semester Course

Course Number: IS 6482
Credits: 3

This course introduces data mining technologies that assist in discovery of reliable, understandable and useful patterns in structured, semi-structured and unstructured data. Students will practice core data mining technologies, analyze cases, and explore real world applications and issues.

Fourth Semester Courses

Course Number: MKTG 6620
Credits: 3

Business Analytics is a strategic asset that offers unique opportunities for competitive advantage. It lives at the crossroads of business and technology. As technologies transform the marketplace, companies across the globe are collecting an enormous amount of data that can be used to predict the consequences of alternative courses of action and guide decision-making. The objective of this course is to i) demystify the world of big data analytics and ii) show applications of analytic tools across everyday business decisions. Some of the topics discussed will be applications of supervised, unsupervised machine learning, outlier analysis, and adaptive learning.

Course Number: MKTG 6602
Credits: 1.5

This is a graduate-level course on causal inference to support business decisions. The course focuses on techniques for analyzing data from observational studies to avoid biases and accurately estimate treatment effects. By the end of this course, students will have a solid foundation in causal inference, equipped with the tools and knowledge needed to estimate causal relationships and solve real-world business problems.

Course Number: OSC 6610
Credits: 1.5

Managerial decisions – regardless of their functional orientation – increasingly leverage quantitative models to approximate business problems and provide insights. This course takes a managerial approach to analytical modeling to analyze problems in a wide array of fields such as finance, marketing, operations, information systems, etc. The tools covered in this class are deterministic optimization techniques including linear programming, network models and integer programming.

Course Number: OSC 6611
Credits: 1.5

Like OIS 6610, this course takes a practical approach to analytical modeling. While the emphasis in Analytical Decision Models 1 was on deterministic optimization techniques, models in the course will be probabilistic in nature. The main topics for the course are advanced queuing and simulation. Applications will encompass problems from a variety of business disciplines including production/operations management, marketing, and finance.

Course Number: IS 6813
Credits: 1.5

Students work in teams to complete an analytics project for a business or nonprofit organization. Sponsor organizations represent a variety of industries, including operations, healthcare, and marketing. Students capture, organize, and analyze data — ultimately making recommendations to their sponsor organization based on their findings.

First Semester Courses

Course Number: MKTG 6487
Credits: 1.5

This course introduces students to the main elements of business analytics. The domains include framing the business problem, framing the analytics problem, managing data, selecting the methodology, building the model, deployment and life cycle management. Students will learn and practice the domains using small projects.

Course Number: IS 6420
Credits: 3

Advanced topics in database theory and design, including hands-on development of a working database system. Topics covered include the relational database model, foundations in relational algebra, design techniques, SQL, distributed databases, multimedia databases, and knowledge bases.

Course Number: IS 6489
Credits: 3

This is a graduate level course in statistics, with an emphasis on developing predictive models using an open source statistical programming language. The engaged student should expect to develop foundational skills for data analysis. Topics covered will include some or all of the following: descriptive statistics, nonparametric regression, probability distributions, linear and logistic regression, tree-based methods, model assumptions and model checking, cross-validation, simulation, resampling, visualization, and reproducible research.

Course Number: IS 6491
Credits: 1.5

Data Visualization is the graphical representation of information. Data Visualization and related technologies create value within organizations by providing insight from complex sets of data by communicating key aspects therefrom. This course focuses on how to increase the likelihood of action based on insights from data by telling stories with data that leverage effective Data Visualizations. The course includes a mix of theory and hands-on application using contemporary processes and Data Visualization technologies.

Second Semester Course

Course Number: IS 6493
Credits: 3

Successful corporations can utilize data science techniques to help drive business decision making by analyzing datasets of varying sizes. In this course, a hands-on practitioner’s approach is taken to learning the fundamental knowledge, techniques and tools required for leading data science teams and analyzing big data. This course will utilize popular open source technologies and libraries in use today to learn how to collect, pre-process and visualize data, as well as build and test models for inference and prediction. We will examine the unique challenges posed by big data and complex models, and learn how to address them using distributed computing frameworks such as Dask, Hadoop and Spark. The course is taught in Python, and will offer a bootcamp in the first few weeks to help everyone get comfortable with the language.

Course Number: IS 6482
Credits: 3

This course introduces data mining technologies that assist in discovery of reliable, understandable and useful patterns in structured, semi-structured and unstructured data. Students will practice core data mining technologies, analyze cases, and explore real world applications and issues.

Third Semester Courses

Course Number: MKTG 6620
Credits: 3

Business Analytics is a strategic asset that offers unique opportunities for competitive advantage. It lives at the crossroads of business and technology. As technologies transform the marketplace, companies across the globe are collecting an enormous amount of data that can be used to predict the consequences of alternative courses of action and guide decision-making. The objective of this course is to i) demystify the world of big data analytics and ii) show applications of analytic tools across everyday business decisions. Some of the topics discussed will be applications of supervised, unsupervised machine learning, outlier analysis, and adaptive learning.

Course Number: MKTG 6601
Credits: 1.5

This is a graduate-level course on causal inference to support business decisions. The course concentrates on how to design, size, and analyze simple but effective experiments.

Course Number: OSC 6610
Credits: 1.5

Managerial decisions – regardless of their functional orientation – increasingly leverage quantitative models to approximate business problems and provide insights. This course takes a managerial approach to analytical modeling to analyze problems in a wide array of fields such as finance, marketing, operations, information systems, etc. The tools covered in this class are deterministic optimization techniques including linear programming, network models and integer programming.

Course Number: OSC 6611
Credits: 1.5

Like OIS 6610, this course takes a practical approach to analytical modeling. While the emphasis in Analytical Decision Models 1 was on deterministic optimization techniques, models in the course will be probabilistic in nature. The main topics for the course are advanced queuing and simulation. Applications will encompass problems from a variety of business disciplines including production/operations management, marketing, and finance.

Course Number: IS 6812
Credit: 1.5

Students prepare for their capstone project through targeted career development and practice projects in business analytics. Students will leave this course prepared to complete a real-world analytics project for a business or nonprofit organization.

Fourth Semester Courses

Course Number: MKTG 6640
Credits: 3

The web is replete with unstructured data in the form of emails, tweets, blogs, customer reviews, and so on. This course introduces the tools and concepts that allow businesses to analyze textual data and shows how these methods can be used to make better business decisions in online and offline contexts. The course focuses on different analytical techniques and algorithms including preprocessing, text classification, text clustering, topic modeling, document summarization, and sentiment analysis. These methods are applied to different areas of business including marketing, human resource management, business law, accounting, and finance.

Course Number: MKTG 6602
Credits: 1.5

This is a graduate-level course on causal inference to support business decisions. The course focuses on techniques for analyzing data from observational studies to avoid biases and accurately estimate treatment effects. By the end of this course, students will have a solid foundation in causal inference, equipped with the tools and knowledge needed to estimate causal relationships and solve real-world business problems.

Course Number: IS 6813
Credits: 1.5

Students work in teams to complete an analytics project for a business or nonprofit organization. Sponsor organizations represent a variety of industries, including operations, healthcare, and marketing. Students capture, organize, and analyze data — ultimately making recommendations to their sponsor organization based on their findings.

Recommended Preparation

Students who are new to information systems should consider studying foundational concepts before starting the program. There are numerous free and affordable resources available online. A popular option is this free online CLEP Information Systems course from Modern States.

  • NEW

    Explore Your Options

    Join us online to learn which business technology program aligns with your goals. We’ll compare curriculum and career paths for Business Analytics, Cybersecurity, and Information Systems.

    Wednesday, May 27 | 

    5:30 pm - 6:00 pm MT

     | Online

Specialization Opportunities

Our goal is to see students land top jobs in business analytics; Specialization opportunities can give students a competitive edge and help them stand out in the job market.

Concurrent Degrees

Our MSBA program can be completed concurrently with another graduate degree for a robust, multifaceted education.

Students can pursue an MBA concurrently with an MSBA. Accelerate your career in business management by combining both degrees. You’ll exit the program with a world-class education and the quantitative skills needed to immediately advance your career.

Completion Time

Students can pursue a Master of Science in Business Analytics (MSBA) concurrently with a Master of Science in Finance (MSF). Students who pursue both degrees will possess a unique skillset, blending financial acumen with the ability to extract actionable insights from data, creating opportunities in areas like quantitative finance, risk management, and investment analysis. As the financial industry increasingly relies on data-driven decision-making, graduates could lead the way in developing innovative financial models and strategies.

Credit Hour Breakdown

  • MSF Credits: 30
  • MSBA Credits: 30
  • Total Credits: 60

Completion Time

  • Full-time students can complete the program in 7 semesters.
  • Part-time students can complete the program in 10 semesters.

Learn More About MSF

Let’s Connect

Our admissions specialists are happy to discuss any questions or concerns you might have about the application process, admissions requirements, or any other aspect of the program. We’re eager to meet you and provide personalized support as you explore our program.

MSBA@Utah.edu
David Eccles School of Business
Robert H. and Katherine B. Garff Building, Room 2350