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Overview

MSBA – Academics
Master the four competencies crucial to the digital economy

UC Davis MSBA ranked Top 25 WorldWide Our interdisciplinary UC Davis faculty will prepare you to thrive as an innovative leader in this data-driven era. Through our program you will build competencies in analytics, data, business and practice.  


Program Highlights

  • Blends data science skills with business knowledge and organizational savvy.
  • Recognizes and leverages the importance of a diverse set of skills in analytics work, by admitting students based on leadership in a salient skill set (quantitative analysis, computing, business experience or organizational effectiveness) and bringing them together in teams with leaders who have complementary strengths.
  • Emphasizes learning-by-doing and teamwork through a live analytics project with a corporate sponsor and faculty mentors. Integrated with your studies, the project scope runs from opportunity identification, to problem structuring, to solution, persuasion and implementation.
  • Inspires learning from student peers with backgrounds in analytics and humanistic disciplines, graduates and mid-career professionals, from the U.S. and abroad.

Core Competencies

Business

Build your business skills for achieving organizational impact through leadership, strategic thinking, communication and project management. 

Data

Build your data skills as you learn core methods for acquiring, storing, handling and representing data.

Analytics

Analytics is the discovery, interpretation and communication of meaningful patterns of data. Build your skills in core statistical and computational techniques such as data modeling, databases, regression, data mining, machine learning and operations research methods.

Practice

Our program will give you hands-on experience working with and implementing analytic projects in a business environment. Under the direction of program faculty and in small student teams, you will learn analytics by navigating the complete project lifecycle on business problems drawn from real industry data.

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