Online Masters of Science in Data Science and Analytics
- 33Credit hours over 14 months
- $24,000Estimated 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.
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.
ISE 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).
ISE 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.
CS 4413: Algorithm Analysis
The aim of this course is to provide a comprehensive introduction to strategies and tools for analyzing algorithms of various types including serial vs. parallel, centralized vs. decentralized, deterministic vs. random, and parallel circuit based vs. conventional software based, etc. Three basic strategies for the design of algorithms are described and illustrated- (1) divide and conquer, (2) greedy methods and (3) dynamic programming. These strategies use a variety of problems from sorting, searching, merging, data compression, matrix chain product, Graph problems, Monte Carlo vs. Las Vegas type algorithms, integer add and multiply, etc. We will cover the basics of complexity theory by introducing the notions of P, NP and NPC and NP-hard class of problems and discuss examples of polynomial time approximation to NPC problems. This is a senior/first year graduate level core course in Computer Science.
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Rich and Engaging
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Meet the Faculty
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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.
- 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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