MBAn Curriculum

Students begin with required coursework in machine learning, optimization, analytics, organizational dynamics, and technical tools. Students may also select one unrestricted elective.

Machine Learning (12)
Develops algorithms for central problems in machine learning from a modern optimization perspective.

Intensive Hands-on Deep Learning (6)
Fast-paced introduction to deep learning, the engine behind modern artificial intelligence, with an emphasis on developing a practical understanding of how to build models to solve complex problems involving unstructured data. Rapid introduction to deep neural networks and their training. Emphasis on language models, focused on masked and generative modeling and vision models (using diffusions and transformers).

Optimization Methods (12)
Introduces the principal algorithms for linear, network, discrete, robust, nonlinear, and dynamic optimization. Emphasizes methodology and the underlying mathematical structures.

Advanced Analytics Edge (6)
Examines how data analytics transforms businesses and industries, using examples and case studies in e-commerce, healthcare, social media, high technology, sports, the internet, and beyond.

Analytics Lab (9)
Student teams design and deliver a project using analytics, machine learning, large data sets, or other digital innovations to create or transform a business or other organization. Culminates with presenting results to an audience that includes IT experts, entrepreneurs, and executives.

From Analytics to Action (6)
Develops an appreciation for organizational dynamics and competence in navigating social networks, working in a team, demystifying rewards and incentives, leveraging the crowd, understanding change initiatives, and making sound decisions.

Analytics Tools (3)
Get familiar with different analytics topics, programming languages, and software tools. (Python, R, Julia/JuMP, GitHub, etc.)

+ One Optional Unrestricted Elective (0-12 units)

During IAP, MBAn students complete required courses and workshops while beginning the Analytics Capstone matching process. Students participate in the Capstone kickoff and company pitches, then prepare for project interviews and matching.

Analytics Tools Workshop (3)
Get familiar with different analytics topics, programming languages, and software tools. (Python, R, Julia/JuMP, GitHub, etc.)

Analytics Capstone (3)
Students begin the Analytics Capstone matching process by attending the Capstone kickoff and company project pitches. They interview with potential host organizations and, after matching, begin defining the project’s goals and scope.

Communication and Persuasion through Data (3)
Focuses on the strategic and tactical use of data to move others to take (the correct) action. Sharpens communication skills via practice and real-world examples.

AI Strategy, Governance, and Ethics (3)
Examines how artificial intelligence is reshaping organizational strategy, governance, and ethical responsibility. Introduces frameworks for managing AI strategy and risk, ensuring AI systems align with enterprise values.

Students continue their Analytics Capstone and complete between 27 and 48 units of Analytics Electives. At least 27 units must come from the approved Analytics Electives list, which is shared in November before course bidding.

Analytics Capstone (12)
Student teams work on campus with their host organizations and faculty advisors to analyze data, develop methods, and refine their proposed solutions. Teams maintain regular contact with their sponsors as they prepare for the full-time summer phase of the project.

+ Analytics Electives

Students complete the final phase of the Analytics Capstone through full-time work with their host organizations.

Analytics Capstone (9)
Students work full time with their host organizations to complete their analytics projects and deliver their findings. The Capstone concludes with a final report and presentation in August.

• Model flu epidemics and optimize flu vaccine distribution internationally, utilizing mixed integer optimization techniques applied to sparse regression (D. Bertsimas)
• Analyze airline pricing for legendary carriers versus low-cost carriers to identify any oligopolistic pricing power (A. Barnett)
• Forecasted used car replacement costs using various models (LSTM, decision tree, SVM, SARIMAX) to better understand trends impacting replacement valuation methodology (G. Perakis)
• Expanded unstructured food safety database of 4M test results in China, developed NLP classification algorithm (Gensim, Fasttext) to increase database coverage by 17% (R. Levi)
• Design and implement scalable algorithms to impute high-dimensional matrices on 500k insurance data with $1.2B revenue (V. Farias)
• Predicting crop prices and optimizing network flow for rural farmers of India (K. Zheng)

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