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Data Science and Business Analytics

Data Sciences and Business Analytics Concentration

Concentration Coordinators Sanjiv Das (Finance), Xiaojing Dong (Marketing), John Heineke (Economics)

Many new opportunities exist for managers to apply analytical techniques to business problems. This area has experienced explosive growth due to the availability of large corporate databases, enhanced computing tools, and the analytical culture of many Silicon Valley companies. 

Mission Statement This concentration will provide MBA students with analytical and data skills, which are embedded in business knowledge. Graduates will be prepared  i) to link company data resources with knowledge of markets to address a wide range of business problems, ii) to offer market based perspectives and solutions where business intelligence is a fundamental driver of corporate value, and iii) to interface with engineering teams to deliver data-driven solutions.

A large number of major Silicon Valley companies are managed by engineers and computer scientists, creating an expectation that managers throughout the organization are able to apply the tools of modeling and data analysis to business problems. This concentration is intended to provide students with the perspective, skills, and methods for interacting with, and being an interface between, business and data science professionals in companies in the application of data science and analytics solutions to business problems. A substantial number of our MBA students come with an interest and aptitude for analytical techniques due to their technical undergraduate degrees and related work experience.

Students in this concentration will need a strong background in probability, statistics, regression analysis, and calculus.

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 has created a demand for managers with both business and analytical skills throughout the organization.

The concentration is interdisciplinary and meets a growing need for knowledge workers in the Valley who employ quantitative methods, economic paradigms, and technology to business processes and decision making.

Learning Objectives

  1. Develop critical thinking skills for strategic evaluation and implementation of current data science (and big data) paradigms.
  2. Understand and acquire technical expertise in various quantitative fields such as statistics, econometrics, calculus, optimization, and software paradigms, that underlie various analyses undertaken by corporations.
  3. Learn how to build models (theoretical, statistical and econometric) to characterize business situations, develop strategies, and analyze these models, while collecting, verifying, and using data to achieve enhanced business decisions. 
Courses fulfilling concentration

Select five courses from those listed below:

COURSE # Course Name Units
ECON 3422 / MKTG 3588 Profit-Focused Pricing 3
ECON 3430 Game Theory 3
ECON 3436 ECON 3436 3
FNCE 3484 Financial Engineering 3
FNCE 3490
(previously 3696)
Data Science and Business Analytics
(previously Data Science: Analytics and Big Data)
MKTG 3597
(previously 3696)
Marketing Analytics 3
MKTG 3802 Mobile Marketing and mCommerce 3
IDIS 3802 Data Science Analysis with Python 3
OMIS 3366 Database Management Systems 3
OMIS 3386 Business Intelligence and Data Warehousing 3

Job Possibilities: Data Science Concentration Graduates

Positions graduates will be ready for range from highly technical business data analysts to entry level data scientists. A person working in a data science environment today needs to know how (a) to ask relevant and sharply focused questions of business data, (b) to access data and use software languages to answer those questions, (c) to present results and visualizations, (d) to communicate effectively with non-technical management, (e) to understand business paradigms in the field, and (f) to critically evaluate the work of data scientists. Our academic advisory board for the concentration is made up of top data scientists at LinkedIn, Yahoo!, Macys, and Acxiom. They suggest that this chain of skills is critically needed. They believe that data scientists not only include computer scientists well versed in Big Data, but also those who understand markets, the tools of data science, the paradigms of business, and are able to pose fundamental business questions.

For example, it is not important to know the technical aspects of cloud computing, but one should know what it is and where it fits into the business strategies of firms. It is not important  to be able to write sophisticated software or regression analysis tools, but it is necessary to know how to design and undertake regressions using the appropriate software to thoughtfully address the question at hand. Extensive knowledge of database engineering is not needed, but being able to extract data from databases to run analyses is needed. Modern day data analytics goes well beyond running Excel, so the skill set must include stronger analytic tools.

When a student  is able to pose good questions, identify basic problems, and has the skills to use data well, within the appropriate economic paradigms, the student’s skill set will be applicable to a wide set of disciplines in business. The spillover benefits are wide-ranging, as students will find that courses in this concentration prepare them to be much more adept, both conceptually and technically, in other courses in the usual disciplines of marketing, finance, economics etc.


Last update July 2015