Full-time MSBA

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.

Course Descriptions

The full-time MSBA program begins in June each year and consists of three intensive semesters. Each semester contains 15 credits of coursework for a total of 45 graduate degree credits. Classes for the full-time MSBA  program meet during the day.

Summer Semester Focus: Business and Management Fundamentals

MSBA 6250 Analytics for Competitive Advantage (3 credits)

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 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 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.

MBA 6030 Financial Accounting (3 credits)

Basic principles of financial accounting, involving the consecution/interpretation of corporate financial statements. 

MBA 6210 Marketing Management (3 credits)

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.

Fall Semester Focus: Technical Fundamentals

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 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 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 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 neural network and their applications in structured and unstructured data.

Spring Semester Focus: Advanced Analytics Courses and Experiential Learning

MSBA 6430 Advanced Issues in Business Analytics (3 credits)

Analysis of unstructured data, fundamentals of text mining, sentiment analysis; fundamentals of network analysis, mining digital media and social networks, peer effects and social contagion models; personalization technologies and recommender systems.

MSBA 6440 Data-Driven Experimentation and Measurement (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 6450 Modeling and Heuristics for Decision Making and Support (3 credits)

Fundamentals of decision analysis, optimization, linear and integer programming, risk analysis, heuristics, simulation, decision technologies.

MSBA 6345 Project 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 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

  • Access to High Tech Facilities

    The Carlson School is housed in two state-of-the-art buildings on the picturesque University of Minnesota campus, and come equipped with the latest technology in the classroom, laboratory, and beyond.

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