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This course is an extension of BUS 232. It develops and applies the quantitative models that are most directly relevant to business decisions. Beginning with material on multiple regression and forecasting modeling, the course moves on to decision analysis, business simulation, quality control, and an introduction to optimization. Prerequisite: MATH 150, MATH 151, MATH 154, or MATH 157; BUS 232 or STAT 270; 45 units. Quantitative.

Prepares students to model, analyze and propose improvements to business processes. In the major project, students analyze a process within an organization and use current techniques and tools to propose changes and a supporting information system. Prerequisite: BUS 237; 45 units; OR Data Science majors with 45 units. Students with credit for BUS 394 may not take this course for further credit.

Exposes students to the art of using analytic tools from across the spectrum of data mining and modeling to provide powerful competitive advantage in business. Students will learn to recognize when a method should or should not be used, what data is required, and how to use the software tools. Areas covered include database marketing, geospatial marketing and fundamental strategic and tactical decisions such as segmentation, targeting and allocating resources to the marketing mix. Prerequisite: BUS 343, 336, 360W, 60 units; OR Data Science majors with BUS 343, 360W, and 60 units.

Utilizes technology to support analysis and decision making abilities by identifying, analyzing and effectively reporting important business information. Concepts of data warehousing, data mining and visualizing data are introduced. A variety of software applications are used to demonstrate tools and techniques that support analysis and decision making for managers. Prerequisite: BUS 336, 360W; 60 units. Corequisite: BUS 336 can be taken concurrently.

Focuses on the design and use of integrated database management systems in organizations. Students create data models for capturing and storing data from business operations, organizing it for deriving business intelligence, aggregating and visualizing the information for decision-making. Structured query language is primarily used for all the above data management activities. Prerequisite: BUS 360W, 362; 60 units.

A course in the management of marketing research. The basics of the design, conduct, and analysis of marketing research studies. Prerequisite: BUS 343, 336; 45 units; OR Data Science majors with BUS 343 and 45 units. Students with credit for BUS 442 may not complete this course for further credit.

Development and use of simulation models as an aid in making complex management decisions. Hands on use of business related tools for computer simulation. Issues related to design and validation of simulation models, the assessment of input data, and the interpretation and use of simulation output. Prerequisite: BUS 336, 360W, 60 units; OR Data Science majors with BUS 360W, 60 units.

A seminar in the use of Bayesian techniques in business decisions. Prerequisite: BUS 336, 360W; 60 units; OR Data Science majors with BUS 360W and 60 units.

Adopting an experimental approach and being responsive to customer and competitor reactions is an essential skill set to firms and organizations. Situated in the data-rich environment of digital media and channels like websites or search engines, this course is designed to help students develop "probe and learn" skills, which translate beyond web and digital management, and help them acquire hands-on experience in using analytics tools to manage digital marketing campaigns. Prerequisite: BUS 360W, BUS 343; 60 units.

Examines complex, real-world decision making issues using an evidence-based approach that employs decision making strategies involving statistics, data management, analytics, and decision theory. Through a major decision making project within the community, students will experience first-hand the process of consultation, data acquisition, analysis, and recommendation. The data in the project will be proprietary to the community partners and students thus need to sign a non-disclosure agreement. A non-disclosure agreement template is attached to the course outline. The results of the project will remain the intellectual property of the students; notwithstanding, those results will be shared with the data provider. Students also have an option to complete a project with non-proprietary data. Prerequisite: BUS 345 or BUS 440, BUS 360W, BUS 437 or BUS 441, BUS 445, BUS 462, and BUS 464; 90 units; OR Data Science majors with BUS 360W, BUS 445, CMPT 354 and 90 units.