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Data Science and Business Analytics
Goals: Provide students with the perspective, skills and methods for applying data science and analytics to business problems. The motivations for this concentration are:
1. The local business environment: For many companies business competitiveness and decision making depend on applying analytical techniques to large corporate databases (big data) often using enhanced computing tools, such as cloud computing.
2. The analytical culture of companies in Silicon Valley: Many major companies here are managed by engineers and computer scientists. This creates a demand for managers with analytical skills throughout the organization.
This concentration is inter-disciplinary. It meets a growing need for knowledge workers in the Valley who employ quantitative methods, economic paradigms, and technology to business processes and decision making. The student who specializes in this concentration will have strong analytical and data skills combined with business knowledge that will be applied to solving problems where business intelligence is a fundamental driver of corporate value.
1. Understand and acquire technical expertise in various quantitative fields such as statistics, econometrics, stochastic processes, calculus, optimization, and software paradigms, that underlie various analyses undertaken by corporations.
2.Learn how to build models (theoretical and econometric) to characterize business situations, develop strategies, and analyze these models collecting, verifying, and using data to achieve optimal business decisions.
Course RequirementsThe concentration requires completing 15 credits, A required one-unit introductory course and a required three-unit class, and an additional 11 units from the list below, with at least three units from each of the two course categories below:
While the courses above are not prerequisites to the remaining courses, students are strongly advised to take them early in the concentration.
Students may consider completing related courses, such as from the following suggested tracks: