Data and the use of data in business decision making is ubiquitous. The need for data analytics is an ever-growing, increasingly important, skill set for both the current and future workforce.
The Master of Science in Data Analytics degree is a STEM-designated program that prepares students to meet ‘Big Data’ business challenges. Graduates emerge well-prepared to apply data analysis, visualization, and artificial intelligence prediction tools to create maximum value and expand market opportunities for organizations.
Along with a core data analytics curriculum, students select one area of business concentration.
A course designed to teach the fundamentals of programming as it relates to the development of data science and analytics solutions. May include software platforms Python or R.
Focuses on the identification and access of information sources and analyzing the information to make informed decisions and solve managerial problems. Among the topics included are numerical and graphic description of data, confidence intervals, hypothesis testing, regression analysis and predictive modeling, linear allocation models and allocating resources, forecasting, and decision analysis. The course utilizes spreadsheet, statistical and simulation software.
A course on communication of analytical and data science results using visualization software. May include Tableau, PowerBi and other visualization packages.
An integrated study of systems for collecting, storing and retrieving data with a particular emphasis on relational databases. May include Snowflake, SQL SERVER, MySQL.
Addresses tools and techniques required for analyzing business data for forecasting. Includes time series analysis and time series forecasting, and application of these techniques to support business decision makers.
Addresses practices related to predictive modeling (decision tree, regression, neural network, ensemble and boosting models, among others). Includes modifying data for better analysis results, model training and testing, machine learning methods, comparing and explaining complex models, generating predictions and communicating results to help make better business decisions.
Addresses concepts, tool and techniques for using large datasets to address business problems. Includes understanding big data concepts, common architectures, and using industry-standard tools to store, query, transform and analyze large datasets. Techniques related to importing and working with diverse types of data across different technical environments discussed and practiced.
To complete the 30-credit-hour degree requirements, students can choose from nine credit hours from existing disciplines including MS in Accounting, MS in Financial Analytics, MBA in Leadership and MBA in Health Care Administration.
Prerequisites are not required.
Applicants must demonstrate a basic understanding of analytical techniques and have strong communication skills.
Applications are evaluated on an individual basis considering the following factors:
1. Academic Background (transcript)
2. Professional Experience (resume or CV)
For questions about the application process:
Cindy Treadway
Meinders Graduate Advisor
[email protected]
405-208-5154
For questions about the international application process:
Aaron Wheelbarger
Director of International Admissions
[email protected]
405-208-5006
Located minutes from downtown Oklahoma City, the Meinders School of Business contains cutting-edge technology in more than a dozen teaching rooms, four executive classrooms, large and small conference rooms, four computer labs, including the Bloomberg Finance Lab for access to the latest investment data, breakout rooms for small-group meetings, and a 250-seat, theater-style auditorium.
