Master in Applied Data Science and Data Analytics
Drive Innovation through Data Science and Data Analytics
Make 2023 your year! Apply by January 31 to start classes in February.
Kettering University’s innovative, 100% online MS in Applied Data Science and Data Analytics program represents a fusion of statistical, data management, and computing technologies—including data mining, machine learning, cloud computing, and visualization - at a total tuition cost of $25,000.
This unique program prepares leaders to achieve organizational goals through the power of data, equipping students to:
- Manage and direct teams to create, define and refine data
- Design and build new processes for data modeling and production using prototypes, algorithms, predictive and quantitative models, and custom analysis
- Unlock solutions hiding in data
- Effectively communicate data science concepts, results, and visualizations
- Pursue leadership roles in data science and analytics applying well-rounded professional knowledge and skills
Fast Facts
- 100% online, 40-hour curriculum – 10 courses including a capstone course
- Foundations in Data Science graduate certificate – awarded after completion of the first three required courses
- Classes incorporate industry software, languages, and tools – including Python and R
- Tuition Cost – $625 per credit hour - financial aid and military benefits are available to those who qualify
- Multiple intakes a year - Fall, Winter, Spring, and Summer
- Finish in as few as 12-24 months - no cohorts
- No GRE/GMAT required
An Unwavering Commitment to Quality
KUO is accredited by the Higher Learning Commission and a member of the North Central Association of Colleges and Schools. Our dedicated faculty and staff are committed to providing quality education and shaping some of the brightest minds of tomorrow.
- #6 Best Regional Universities Midwest U.S. News & World Report- 2022-2023
- #12 Best Colleges for Veterans U.S. News & World Report- 2022-2023
- One of the Best 387 Colleges in the United States Princeton Review - 2022
- Silver-Status University - Veteran-Friendly Schools (VFS) program Michigan Veterans Affairs Agency - 2022
About Data Science and Data Analytics
Data Scientists are highly skilled in analysis and prediction. Using scientific inquiry through algorithms and computing systems, data scientists gather intelligent insights from unstructured, complex data to provide actionable information for decision-making based on its predictive power.
Career Outlook for Data Science and Data Analytics:
- Data scientists will experience a 15% increase in demand for their expertise1, with significant growth in opportunities projected through 2029.
- More than half of data scientists have master’s degrees, a 20% increase since 2019.2
- Careers in data science are some of the highest-paying and in-demand roles within the U.S. and beyond.
Data scientists are represented in virtually every industry, including IT, engineering, medicine, health care, social sciences, biological sciences, business, economics, insurance, finance, marketing, social media, security, defense, geolocation and more.
Schedule an Appointment with an Advisor
Click the link below to schedule an appointment with our Enrollment Advisors to get more information on the entire admissions process.
Concepts, principles, issues and techniques for big data and cloud computing provide a foundation on data curation and statistical analysis. The primary goal of this course is to introduce data analysis concepts and techniques that facilitate making decisions from a rich data set. Students will investigate big data concepts, metadata creation, interpretation, and basics of information visualization.
The objective of this course is to provide real world problems to graduate students to apply knowledge gained from academic studies in Data Science. Students select a problem in an area of interest and are supervised by faculty. Academic objectives are to understand the scope and overarching goals of the assignment, indicate competency in analytical skills, understand ethical conduct, and demonstrate curiosity and value creation.
When executed well, visualizations enhance oral or written communication by supporting arguments and claims, providing insight into complex issues, and supporting recall and decision-making in audiences. In this course, students become familiar with common genres of visualization, techniques for designing them effectively and ethically, and how to present them orally and in prose.
Information retrieval and data mining topics, including information storage and retrieval, file structures, precision and recall, probabilistic retrieval, search strategies, automatic classification, automatic text analysis, decision trees, nearest neighbor method, and rule induction.
This is a calculus-based introduction to probability theory and stochastic modeling. Students will learn fundamentals of probability, discrete and continuous random variables, expectation, independence, Bayes' rule, important distributions and probability models, joint distributions, conditional distributions, distributions of functions of random variables, moment generating functions, central limit theorem, laws of large numbers.
A study of statistics including point and interval estimation, consistency, efficiency and sufficiency, Minimum Variance Unbiased Estimators, Uniformly Most Powerful tests, likelihood ratio tests, goodness of fit tests, an introduction to nonparametric methods, and Linear models.
A comprehensive overview of cloud computing and its application to big data and data science. Current technologies that comprise the concept of cloud computing are discussed. Exploration of major Cloud frameworks that support large data storage and applications that support data analytics.
Database design and implementation, entity-relationship model, relational model, relational query languages, physical data organization, XML, distributed database concepts, Big Data technologies, enhanced data models.
An introduction to machine learning with application to big data. Topics include: supervised learning, unsupervised learning, learning theory bias/variance tradeoffs, VC theory, large margins, and reinforcement learning.
Current topics in Data Science are discussed and analyzed.