Santa Clara University

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.

Learning Objectives:

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 Requirements

The 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:

Required Introductory Courses
1. Introduction to Data Science (New)
2. Data Analysis and Econometrics in R (New)

While the courses above are not prerequisites to the remaining courses, students are strongly advised to take them early in the concentration.

Analytical Courses
3. Game Theory (ECON 430)
4. Financial Engineering (FNCE 484)
5. Mathematical Finance (FNCE 696)
6. Advanced Topics in Stochastic Processes, Monte Carlo Simulation, and Credit Models (three 1-unit classes, FNCE 711, 710, 712)
7. Financial Instruments and Markets (FNCE 488)
8. An Introduction to the Mathematical Foundations of Microeconomics (New)
9. Pricing and Revenue Optimization (New)
10. Dynamic Optimization in Economics and Management (New)

Data Courses
11. Topics in Profit Maximizing Pricing (Econ 422/MKTG 588)
12. Marketing Analytics (MKTG 696)
13. Data Science and Business Analytics (FNCE 696)
14. Computer Simulation and Modeling (OMIS 362/ MSIS 626)
15. Business Intelligence and Data Warehousing (OMIS 386)
16. Web programming (MSIS 696)
17. An Introduction to the Mathematical Foundations of Microeconomics (New)

Students may consider completing related courses, such as from the following suggested tracks:

• Marketing Science and Analytics:
  MKTG 696 - Marketing Analytics
  ECON 422/MKTG 588
  OMIS 386/ MSIS 621
  ECON 430
  ECON 696 (An Introduction to the Mathematical Foundations of Microeconomics)
  OMIS 696 (Pricing and Revenue Optimization)
  

 

• Finance Analytics:
  FNCE 488
  FNCE 484
  FNCE 696 (Data Science and Analytics)
  FNCE 696 (Mathematical Finance)
  FNCE 710, 711, 712
  

 

• MIS Analytics:
  OMIS 386/ MSIS 621
  MSIS 696 (Web Programming)
  OMIS 362/ MSIS 626
  ECON 430
  ECON 696 (Dynamic Optimization in Economics and Management)