MS Business Analytics Curriculum
This 10-month, in-person program spans statistics, econometrics, machine learning (ML), marketing analytics, and data management—taught through an AI-first lens. From data mining to econometrics, the curriculum integrates AI across most courses and offers cutting-edge electives for deeper specialization. Students collaborate with faculty and industry partners on real projects, building deployable, production-grade AI and ML solutions in the cloud while strengthening skills in modeling, data storytelling, and strategic insight.
Core Courses
| Course Number & Description | Course Title | Units |
|---|---|---|
| GSB 5518 Statistics background needed for analysis of business data and econometrics. Probability theory, random variables, sampling and sampling distributions, bootstrapping, estimation methods, properties of estimators, hypothesis testing, and confidence intervals. |
Essential Statistics for Business Analytics | 3 |
| GSB 5520 Exploration of data management including relational databases, data warehouses, and NOSQL databases. Foundation for analyzing, designing, implementing and using information repositories in a business environment. Database development life cycle, data modeling, SQL programming, data quality and integration. |
Data Management for Business Analytics | 3 |
| GSB 5530 Exploration of the concepts, tools and techniques of data mining in the business context, using case study and problem-solving approaches. Multidimensional data modeling, predictive analytics, pattern discovery, forecasting, text mining, and data visualization. |
Data Analytics and Mining for Business | 3 |
| GSB 5544 Use of computers for advanced data analysis in business analytics. Computer programming using statistical software, data gathering and cleaning, and machine learning. |
Computing and Machine Learning for Business Analytics | 3 |
| GSB 5598 Collaborative business project with a client organization supervised by faculty. Apply knowledge, and competencies to address a business problem. A formal written proposal must be accepted by the Associate Dean of OCOB Graduate Programs before work begins. The project may last up to one year. |
Project | 3 |
| ECON 5029 Estimation and analysis of econometric models for analyzing business data. Linear regression models, robust standard errors, causal inference, instrumental variables, maximum likelihood estimation, and logistic regression models and extensions. |
Econometrics and Data Analysis | 3 |
Approved Electives
| Course Number & Description | Course Title | Units |
|---|---|---|
| GSB 5501 Advanced individual research planned and completed under the direction of a member of the college faculty. A formal written proposal must be accepted by the Associate Dean of OCOB Graduate Programs before work begins. |
Individual Research | 3 |
| GSB 5510 Principles of data visualization and storytelling. Data visualization tools for different types of data in the context of business analytics. Communication of results for business actionable insights. Software use includes Excel, Tableau and R. |
Data Visualization and Communication in Business | 3 |
| GSB 5516 Analysis of customer information, using a broad range of tools and techniques including predictive, statistical, and optimization models. Integration of data into reporting platforms. Application of findings to marketing decision-making. |
Strategic Marketing Analytics | 3 |
| GSB 5517 Application of business analytics approaches and techniques to understanding and managing human resources. Emphasizes problems addressed using people analytics, including which methods are best and under what conditions, data quality and validity issues, and interpretation in the HR context. |
Strategic People Analytics | 3 |
| GSB 5521 Apply cloud resources for business analytics. Identify business benefits of cloud computing, storage, networking, data management and security. Use web services to analyze big data including query, statistical analysis, machine learning and visualization. |
Cloud Services & Applications for Business Analytics | 3 |
| GSB 5536 Examination of ethical risks raised by data analysis, including data collection, ownership and usage. Philosophical examination of topics raised by data analysis, including consent, privacy, transparency, bias and potential harms from data collection and use. |
Data Ethics for Business Analytics | 3 |
| GSB 5545 Use of computers for advanced machine learning in business analytics. Boosting, ensemble learning, Bayesian methods, and various types of neural networks. |
Advanced Machine Learning for Business Analytics | 3 |
| GSB 5547 Data analytics tools and models in finance including encryption theory, blockchains, and machine learning. Deep dive into Machine Learning (ML) algorithms for financial data anomaly detection, predictive modeling, and back testing. Develop hash functions for encryption. Apply techniques to real-world finance problems and data. |
Financial Analytics | 3 |
| GSB 5551 Monte Carlo simulation. Decision making under uncertainty. Linear and non-linear programming. Model risk. Applications to finance, operations, strategic planning, and marketing. |
Prescriptive Analytics | 3 |
| GSB 5570 Directed group study of special topics for advanced students. Repeatable up to 6 units. The Class Schedule will list topic selected. |
Selected Advanced Topics | 3 |
| GSB 5575 Career development and preparation with specific focus on the impact of organizational structures on the professions of business analytics and data science. Personal marketing in a dynamic technological environment. |
Career Development for Business Analytics I | 3 |
| GSB 5576 Career development and preparation focusing on verbal communication skills for business analytics, and refining interview and presentation skills. |
Career Development for Business Analytics II | 3 |
| GSB 5577 Career development and preparation focusing on collaboration skills for business analytics, emphasizing the identification and application of digital tools that enhance team productivity. |
Career Development for Business Analytics III | 3 |
| GSB 5591 Informing data collection with consumer theory. Utilizing APIs for data collection. Application of the word and text analysis methodology to unstructured textual data. Component-based data mining software Orange3 is covered. |
Developing Consumer Insights with Textual Data | 3 |
For more details about the courses, see the Cal Poly catalog.