Program Structure and Curriculum

MS in Business Analytics

The MS in Business Analytics (MSBA) curriculum balances a depth of analytical training with a breadth of business knowledge. The rigorous and fast-paced program consists of fundamental business courses custom-tailored to data science, technical skills courses, and advanced courses in analytics methods and problem solving. Our MSBA program is recognized by the U. S. Department of Homeland Security as a STEM-designated program.

Rigor & Depth

MSBA graduates discuss the business analytics curriculum at the Carlson School of Management.

Course Descriptions

The MSBA program begins in early June each year and consists of three intensive semesters. Each semester contains 14.5-15.5 credits of full-time coursework for a total of 45 graduate degree credits. Classes for the MSBA  program meet during the day.

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.



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.



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 Agile Management of 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.

Explore Experiential Learning in the Carlson Analytics Lab

Electives (2 credits)

Beyond the Classroom

Experiential learning is a hallmark of the Carlson School. Opportunities to apply classroom learning to real business problems are integrated throughout the MSBA program.

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