• 33
    Credit hours over 14 months
  • $30,000
    Estimated Tuition, will vary by residency
  • 100%
    Online Coursework

About the Program

The University of Oklahoma Gallogly College of Engineering is helping to shape the application of Big Data with its new interdisciplinary Master of Science Degree in Data Science and Analytics (MS DSA).

Organizations large and small employ data scientists to determine profitable lines of business, characterize customers, evaluate and predict risks, improve operational efficiencies, predict system performance, and perform complex simulations. Students in the MS program in Data Science and Analytics will gain a unique perspective into this rapidly growing field. 

The MS DSA degree introduces students to the development of analytical models and methods to extract new knowledge from vast, complex data. Students in the program learn algorithm development from a systems perspective. The interdisciplinary curriculum equips students with knowledge and expertise of the computational methods required to develop, interpret, and transform data into knowledge.

While many companies take advantage of big data to monitor and track all aspects of their business, MS DSA graduates will help revolutionize the way companies compete, produce, and innovate by predicting future operations and behaviors. Graduates will have the skills to design and build tools to extract, assimilate and analyze data, coupled with the systems understanding to predict and enhance future performance for enterprises across all domains of the private and public sectors.

Girl studying

Curriculum

The MS Data Science and Analytics curriculum merges expertise and knowledge from computer science and industrial and systems engineering. Students will develop a strong foundation in the theory and application of data science that will give them the skills to harness big data. Courses in data analysis and analytics will equip the students with the skills to dive deep into the data to find knowledge for systems improvement. Students educated in this program will be able to work end-to-end in the realm of big data.

The curriculum is flexible to meet the needs of the individual student. Full-time and online students are encouraged to seek summer entry in order to streamline their progress to degree. Part-time and on-campus students may enter the program during the summer or fall semesters. All students receive individual advising and can design a plan that is specific to their graduation timeline. The MS DSA degree can be completed as a course work only option or as a research-thesis option. In both options, students have the opportunity to receive course credit for a required professional experience through a practicum course in the data science and analytics field.

  • DSA 5013: Fundamentals of Engineering Statistical Analysis

    DSA 5013: Fundamentals of Engineering Statistical Analysis

    This course provides fundamental concepts in probability and statistical inference, with application to engineering contexts. Probability topics include counting methods, discrete and continuous random variables, and their associated distributions. Statistical inference topics include sampling distributions, point estimation, confidence intervals and hypothesis testing for single- and two-sample experiments, nonparametric statistics, and goodness-of-fit testing. Excel will be used to demonstrate how to solve some class examples, and you'll be expected to use Excel to solve some homework problems. The statistical software package R will be introduced to address very basic statistics problems. Course prerequisites include calculus (differentiation and integration).

  • DSA 5103: Intelligent Data Analytics

    DSA 5103: Intelligent Data Analytics

    Intelligent Data Analytics is an approach to addressing real-world data intensive problems that integrates human intuition with data analysis tools to best draw out meaningful insights.  Topics include problem approach and framing, data cleansing, exploratory analysis and visualization, dimension reduction, regression techniques, tree methods, association mining, and clustering.  Students will be introduced to a powerful open source statistical programming language (R) and work on hands-on, applied data analysis projects.

  • DSA 5113: Advanced Analytics and Metaheuristics

    DSA 5113: Advanced Analytics and Metaheuristics

    This course focuses on developing and employing prescriptive analytics – problem-solving strategies using advanced methods in the context of Data Science and Analytics for complex problems. Topics include both continuous and combinatorial optimization with an emphasis on problem formulation, traditional techniques (e.g. mathematical programming), and modern heuristics such as simulated annealing, evolutionary algorithms, and swarm optimization. Students will use programming skills to implement algorithms and solve problems. Students will demonstate competence in three areas: Identification of pros/cons/issues in a variety of problem types and solution approaches, Collaborative development of solution strategies, Implmentation of advanced anayltics method to solve complex problems.

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Meet the Faculty

  • Kash Barker

    Kash Barker

    Risk Analysis, Systems Engineering

  • Christan Grant

    Christan Grant

    Text Analytics

  • Le Gruenwald

    Le Gruenwald

    Database Systems, Information Security

  • Zhio Kang

    Zhio Kang

    Visual Analytics, Cognitive Engineering

  • Suleyman Karabuk

    Suleyman Karabuk

    Supply Chain, Decision Support Systems

  • S. Lakshmivarahan

    S. Lakshmivarahan

    Algorithms, Time-Series Analysis

  • Amy McGovern

    Amy McGovern

    Machine Learning, Data Mining

  • Charles Nicholson

    Charles Nicholson

    Data Analytics, Optimization

  • Sridhar Radhakrishnan

    Sridhar Radhakrishnan

    Algorithms, Big Data Processing

  • Randa Shehab

    Randa Shehab

    Statistics, Cognitive Engineering

  • K. Thulasiraman

    K. Thulasiraman

    Social Network Analysis, Optimization

  • Theodore Trafalis

    Theodore Trafalis

    Modeling and Optimization, Neural Networks

  • Chris Weaver

    Chris Weaver

    Data Visualization, Database Systems

Let's Get Started

Graduate students at the University of Oklahoma are students who have earned at least a Baccalaureate degree from a regionally accredited university and plan to pursue an advanced degree or a graduate certificate.

Refer to the Application Checklist to learn about the application process and then click Apply Now to get started.

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Application Checklist

  • Complete prerequisites
  • Submit online application
  • Submit transcripts
  • Submit resume and statement of purpose
  • Submit three letters of recommendation
  • Submit GRE score
  • Submit English proficiency (international students)
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Gallogly College of Engineering at The University of Oklahoma

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