MS Business Analytics Curriculum

From data mining to econometrics, our curriculum embraces the latest approaches and developments in the data analytics field. We also offer a number of cutting-edge elective classes that allow students to explore concepts in more detail or develop category-specific skills and knowledge. 

CORE COURSES

Take all of the following:

MS Business Analytics Core Courses
Course Number & DescriptionCourse TitleUnits
GSB 503

Collaborative business project with a client organization that allows graduate level students the opportunity to apply knowledge, skills and competencies to address a business problem. Small teams work in collaboration with a client organization and a faculty advisor. 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.


Collaborative Industry Project4
GSB 518

Statistics background needed for analysis of business data and econometrics. Topics include basics of probability theory, random variables, distribution functions, conditional distributions, independence, expectations, covariance, correlation, random samples, estimation, asymptotic theory, hypothesis testing, and confidence intervals.


Essential Statistics for Business Analytics4
GSB 520

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. Topics include the database development life cycle, data modeling, SQL programming, data quality and integration.


Data Management for Business Analytics4
GSB 530

Exploration of the concepts, tools and techniques of data mining in the business context, using case study and problem-solving approaches. Topics include multidimensional data modeling, predictive analytics, pattern discovery, forecasting, text mining, and data visualization.


Data Analytics and Mining for Business4
GSB 544

Use of computers for advanced data analysis in business analytics. Topics include computer programming using statistical software, data gathering and cleaning, and machine learning.


Computing and Machine Learning for Business Analytics4
GSE 519

Identification and estimation of linear and nonlinear regression models for analyzing business data. Topics include multiple linear regression; model selection; robust standard errors; instrumental variables; maximum likelihood estimation; logit/probit, ordered logit/probit, and other microeconometric models.


Econometrics and Data Analysis4
Core Subtotal:24 units

 

APPROVED ELECTIVES

Minimum of 21 units from the following:

MS Business Analytics Elective Courses
Course Number & DescriptionCourse TitleUnits
GSB 501

Advanced individual research planned and completed under the direction of a member of the college faculty. Designed to meet the needs of qualified students who wish to pursue investigations which cannot be followed effectively in regularly offered elective courses. A formal written proposal must be accepted by the Associate Dean of OCOB Graduate Programs before work begins.


Individual Research1-4
GSB 503

Collaborative business project with a client organization that allows graduate level students the opportunity to apply knowledge, skills and competencies to address a business problem. Small teams work in collaboration with a client organization and a faculty advisor. 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.


Collaborative Industry Project1-8
GSB 510

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 Business4
GSB 516

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 Analytics4
GSB 517

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 Analytics4
GSB 521

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 Analytics4
GSB 536

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 Analytics2
GSB 545

Use of computers for advanced machine learning in business analytics. Topics include boosting, ensemble learning, Bayesian methods, and various types of neural networks. Course may be offered in classroom-based, online, or hybrid format.


Advanced Machine Learning for Business Analytics4
GSB 550

Introduction to Bayesian econometrics with a focus on business decision making. Making appropriate use of prior information; computation of posterior densities; Bayesian forecasting and policy evaluation; model selection and diagnostic tools; alternative loss functions tailored to specific business applications.


Bayesian Econometrics4
GSB 551

Monte Carlo simulation. Decision making under uncertainty. Linear and non-linear programming. Dynamic models of growth and arrivals. Model risk. Applications to finance, operations, strategic planning, and marketing.


Prescriptive Analytics4
GSB 570

Directed group study of selected topics for advanced students. Total credit limited to 8 units. The Class Schedule will list topic selected.


Selected Advanced Topics1-4
GSB 575

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 Readiness in Data Analytics1
Elective Subtotal (minimum):21 units

For more details about the courses, see the Cal Poly catalog.