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Set yourself up for success as a leader who knows how to transform data into impactful business solutions. The Master of Business Analytics (MBAn) curriculum reflects a strategic balance of data analytics and business fundamentals, and includes cross-functional electives in specialty areas like fintech and biotech.
|Data-Oriented Courses||Business-Oriented Courses|
|Summer||Introduction to Data Programming||Analytics Skill Preparation, professional
development and cohort building
|Data Architecture and Acquisition||Business Immersion|
|Advanced Spreadsheets||Software Teams and Project Management|
|Fall||Probability and Statistics||Managerial and Financial Accounting|
|Predictive Analytics||Decision Strategies|
|Data Exploration and Visualization|
|Causal Inference through Experimentation|
|Winter||Prescriptive Analytics||Information Security, Privacy and Ethics|
|Depth Electives||Business Analytics Consulting Studio|
Kick off the program in June with an introduction to both data analytics and business fundamentals and management tools.
Introduction to Data Programming
This course will provide an introduction to computer programming for business analytics applications using a suitable language like Python or R. The course will cover essential programming concepts such as object-oriented programming, control structures and functions with a focus on developing student skills for working with data. The course will further explore essential libraries and packages available for business analytics applications.
Data Architecture and Acquisition
The course will focus on providing students with an understanding of the underlying IT infrastructure including Enterprise Systems and RDBMS Systems - and how to acquire data from those systems for exploration, cleaning, and analysis using tools such as SQL and APIs. The course will help students understand the data generation, storage, and processing ecosystem, the role of enterprise architecture and data architecture, and their impact on decision making.
Spreadsheets are among the most widely used tools in business analytics. This course introduces students to advanced spreadsheet functionalities including financial, statistical, and time/date functions, goal seeking, data tables, and optimization. Students will learn how to develop macros and use powerful Add-I-ins that enhance spreadsheet capabilities.
The objective of this course is to equip students with the appropriate understanding, intuition, and language to understand the larger context within which most business courses reside.
Software Teams and Project Management
This course has two interrelated components: Team dynamics and managing software development projects; presented in an integrated fashion. Team dynamics content will include such topics as the emergence of behavioral norms in project teams, team decision making, potential sources of conflict, and managing conflict constructively. Project management content will include topics such as Lean Startup principles and Minimum Viable Products, development approaches (e.g. agile, traditional waterfall), integrating software tools, and dealing with the inevitable surprise changes to timing, scope, and content.
Jump into Fall Term with core courses like Predictive Analytics and Managerial and Financial Accounting, designed to provide you with the technical expertise you'll need to excel in a business analytics role. Make connections with potential employers during corporate networking, recruiting events, and other industry events held annually at Michigan Ross.
Probability and Statistics
The class will cover fundamental topics in probability and random variables, statistical inference techniques including hypothesis testing, and linear regression. This course will introduce students to different types of limited dependent variable modeling, such as logistic regression, multinomial Logit, ordinal, and Tobit models.
This course introduces students to a supervised learning approach to building predictive models to inform managerial decision-making. The class will build on a foundation of linear regression and logistic regression and extend to machine learning approaches such as decision trees, support vector machines, naive Bayes, and neural networks. The course will address related issues such as bias-variance trade-offs (i.e. underfitting vs. overfitting models) and advanced techniques such as ensemble learning and reinforcement learning.
Data Exploration and Visualization
Data Exploration and Visualization tools enable us to identify important patterns in large data sets and then communicate those patterns and managerial insights in a persuasive visual way for effective decision making. This course will teach the essential tools in exploration and visualization of large data sets. After taking this course, students will be able to work with large datasets and form initial hypotheses based on data exploration and visualization, and effectively communicate their analysis using appropriate visuals.
Unsupervised Machine Learning
This course provides a broad introduction to different unsupervised machine learning algorithms that have potential business applications. The topics covered in this course may include clustering algorithms such as K-means clustering and dimension reduction algorithms such as factor or principal component analysis.
Causal Inference through Experimentation
In making business decisions, managers often need to understand how their strategic and tactical decisions (e.g., a price change) can causally affect outcomes of interest (e.g., revenues). Observational data can help suggest a pattern of relationship between variables but such a relationship may not be causal. In this course, students will learn how to make causal inferences through experimentation. Students will acquire the skills to design controlled randomized experiments (e.g., A/B tests, field experiments) and properly analyze experimental data.)
Management and Financial Accounting
The course explores the use of accounting systems for both external communication (financial) and internal management (managerial). The course will introduce basic concepts used in the construction of corporate financial statements, how to understand those statements, and how to measure key indicators of corporate performance. On the managerial side, the course will cover how accounting information is used internally to measure and track the performance of a firm on its key success factors, and the use of those measures in management decision-making.
Many managerial decisions are increasingly based on analysis using quantitative models. This course will introduce a systematic approach to the value and use of data to make informed decisions. The course will stress fundamental concepts that are important to understand when using data in decision-making; for example the value of historical data (when does the past inform the future, and when not), the value of information (when to gather more, or not), evaluating uncertainty, testing hypotheses with limited information, and understanding the dynamic nature of unfolding information and decision-making.
Round out your requirements and apply what you've learned in our immersive Business Analytics Consulting Studio course. During the course, you'll work in teams to find creative solutions to a pressing business analytics challenge at a real organization, and go onsite to conduct research and present your final recommendation to leadership.
The analytics process seeks to turn large amounts of data into actionable insights, predictions, and recommendations. This course will focus on using optimization modeling techniques to make decisions for different applications of business analytics. This course will focus on the theory and practice of the core optimization methodologies such as linear programming, integer programming, non-linear programming, heuristic models, and simulations. Example applications will be drawn from an array of business functions.
Information Security, Privacy and Ethics
Information security is essential to the business analytics process. This course will introduce students to the ethical and legal issues surrounding business analytics such as consent, anonymity, privacy, data integrity, access and exclusion, algorithmic bias, and relevant regulatory considerations regarding information security and privacy. The course will introduce fundamental concepts of network security, cyber security, potential threats/malware, and policies/practices to manage security threats; and discuss relevant technical aspects of information security such as authentication approaches, data encryption, digital signature, and public key.
In your final term, apply the business analytics concepts you've learned throughout your coursework to an action-based learning project with a real sponsor company. You and your classmates will work in teams to devise creative solutions to a pressing corporate challenge, and present your recommendations to company executives.
Learn how to lead effectively through a high-pressure, high-stakes environment as part of this simulated business crisis.
Unlock your personal capabilities and learn how to increase your influence as part of two transformative Legacy Lab workshops.
Develop executive-level presence and impactful communication skills through storytelling workshops and events hosted by Story Lab.