MSBA Program Curriculum and Structure
Build Essential Analytics Knowledge in One Year
In the full-time MSBA, you’ll complete 45 credits in three semesters. You'll study fundamental topics such as statistics, programming, and data engineering, as well as advanced techniques for data visualization, predictive analytics, machine learning, causal experimentation, and more.
The Carlson MSBA program is recognized by the U. S. Department of Homeland Security as a STEM-designated program.
Start in June, graduate the following May.
Classes meet during the day on campus and in person.
Complete 45 total credits of rigorous, full-time analytics study.
Start in June, Graduate the Following May
Students begin the program in June and graduate the following May, ready to take their new knowledge and skills into the workforce. (Note: Exact dates vary by year; new students are required to attend orientation.)
Comprehensive Analytics Curriculum
In the MSBA program, you'll take 14.5-15.5 credits of full-time coursework each semester. Classes for the MSBA program meet during the day. The program’s cohort model means you’ll start the program at the same time as all other current students and move through the classes together.
Browse business analytics course descriptions below.
Your first semester focuses on fundamentals like statistics, programming, and the use of analytics in today’s business environment. You’ll get to know your classmates throughout the summer as you learn to use tools like Python and R to tackle analytics problems.
MSBA 6110 Business Essentials (3 credits)
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 6120 Introduction to Statistics for Data Scientists (3 credits)
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 modelling. The emphasis is on understanding such analyses for their relevance to decision making.
MSBA 6310 Programming for Data Science (3 credits)
According to recent industry surveys, Python is one of the most popular tools used by organizations for data analysis. In this course, students explore the emerging popularity of Python for tasks such as general purpose computing, data analysis, website scraping, and data visualization. Students first learn the basics of the Python language, then learn how to apply functionality from powerful and popular data science-focused libraries. In addition, participants learn advanced programming techniques such as lambda functions and closures. Most of class time is spent completing practical hands-on exercises.
MSBA 6130 Introduction to Business Analytics in R (3 credits)
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 6320 Data Management, Databases, and Data Warehousing (3 credits)
Fundamentals of database modeling and design, normalization; extract, transform and load; data cubes and setting up a data warehouse; data pre-processing, quality, integration, and stewardship issues; advances in database and storage technologies.
The energy on campus ramps up in fall semester when most other programs begin and the University welcomes new and returning students. For MSBA students, fall means courses in exploratory analytics and data visualization, predictive analytics, and Big Data.
MSBA 6410 Exploratory Data Analytics and Visualization (3 credits)
Fundamentals of data exploration; detecting relationships and patterns in data; cluster analysis, hierarchical and partition-based clustering techniques; rule induction from data; advances in multi-dimensional data visualization.
MSBA 6420 Predictive Analytics (3 credits)
Fundamentals of predictive modeling and data mining; assessing performance of predictive models; machine learning and statistical classification and prediction; logistic regression; decision trees; naïve Bayesian classifiers; support vector machine, ensemble learning, deep learning and their applications in structured and unstructured data.
MSBA 6330 Big Data Analytics (3 credits)
Exploring big data infrastructure and ecosystem, ingesting and managing big data, analytics with big data; Hadoop, MapReduce, Sqoop, Pig, Hive, Spark; SQL for Big data, Machine Learning for big data, Real-time Streaming for big data; cloud computing and other recent developments in big data..
MSBA 6440 Causal Inference via Econometrics and Experimentation (3 credits)
Controlled experiments in business settings, experiment design, A/B testing; specialized statistical methodologies; fundamentals of econometrics, instrument variable regression, propensity score matching.
MSBA 6355 Building and Managing Teams (1.5 credits)
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 6140 Ethics and Data Privacy (1 credit)
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 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.
Because it begins in January, it may be a while before your final semester feels like spring. In the meantime, you’ll keep busy with classes in advanced topics like optimization, as well as electives. You’ll also join the Carlson Analytics Lab, where you’ll work as part of a team of students on a 14-week long experiential learning project for a real client.
MSBA 6430 Advanced Issues in Business Analytics (3 credits)
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 6450 Optimization and Simulation for Decision Making (3 credits)
Fundamentals of decision analysis, optimization, linear and integer programming, risk analysis, heuristics, simulation, decision technologies.
MSBA 6345 Consultative Problem-Solving & Agile Management for Analytics Projects (1.5 credits)
Project management of full-stack analytics projects: identifying deliverables and a methodology; gathering requirements (use cases, user stories); estimating and staffing the project; monitoring project status (earned value and visual methods); team roles in an agile project.
MSBA 6510 Business Analytics Experiential Learning (6 credits)
Hands-on application of analytics methodologies, techniques, and tools learned throughout the program to a real-world problem (such as consulting for a real 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.