Saint Louis University

M.S. in Applied Analytics

Applied Analytics Master’s Degree Online from Saint Louis University

Stephanie Ledesma, M.S. in Applied AnalyticsIf you've wondered why airline prices change every hour, or how Netflix recommends the movie you want to see, or how Facebook finds the stories that interest you most, then you have seen the power of analytics. If you have a mind for mathematics and statistical programming, and a passion for working with data to solve challenging problems, then this is your program.

Analytics is the process of using data relationships and computer models to drive business value, improve decision making, and understand human relationships. Individuals with analytics skills are in short supply. Employer demand for analytics talent is projected to intensify in the coming years as the amount of data continues to grow. Choosing the right graduate degree is an important step in both advancing professional development and enhancing career opportunities and satisfaction.

Saint Louis University's Master's Degree Online in Applied Analytics immerses students in a comprehensive and applied curriculum exploring the underlying data science, information technology and business of analytics. The curriculum is a carefully calibrated and continually updated mix of applied statistics, computing technologies, and business disciplines.

Discover the Meaning within Big Data and become Fluent in Data Analysis

SLU's M.S. in Applied Analytics provides you with skills in data mining, data visualization, predictive analysis, and data management. Upon graduating the program, you will be ready to discover the patterns within large quantities of data and implement solutions from such insights with courses covering the areas of:

  • Programming and Problem Solving
  • Applied Research Methods
  • Applied Analytics
  • Information Retrieval 
  • Information Visualization and Representation
  • Project Management
  • Leadership and Organizational Development

M.S. in Applied Analytics

This online master's degree is a 36-credit program designed for working professionals wishing to pursue a professional master's degree in the Analytics field, while maintaining part- or full-time employment. With courses offered in online and blended formats in accelerated 8-week terms, this program makes advanced education more accessible for working professionals.

Learning Outcomes of Applied Analytics

  • Programming and problem solving to aid various analytical procedures
  • Statistical and analytical techniques for analyzing datasets of various sizes
  • Designing and implementing analytics projects to solve complex organizational problems using evidence obtained systematically from multiple sources
  • Communicate pertinent information effectively in written, oral, and visual forms to facilitate understanding of complex data
  • Project management
  • Leadership and organizational development skills to function effectively in decision-making roles and/or support organizational leaders

Learning Modalities of Applied Analytics

  • Entirely online curriculum of 36 credit hours of which 6 hours consist of the design and implementation of a capstone project in analytics in a business/organizational setting, to emphasize the applied, real-world nature of the capstone course
  • Each course involves completion of weekly assignments and term projects, weekly online interactive sessions with the instructor, recorded video lectures, and discussion forums moderated by the instructor.

If you haven't completed your bachelor's degree, check out the Applied Analytics bridge program.

View the admissions criteria for the Applied Analytics program. Start your application today.

Director of the M.S. in Applied Analytics program: Dr. Srikanth Mudigonda

Courses for M.S. in Applied Analytics

Foundation Courses
credit hours:
Technology and Leadership Development Courses
AA 5250
ORLD 5030
AA 5300
Students should take any 4 of the 5 AA courses, ORLD 5020, ORLD 5030, and either ORLD 5000 or ORLD 5010
credit hours:
Capstone Courses
credit hours:
total credit hours:

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