Master of Science in Applied Data Science and Data Analytics Online
Drive Innovation through Data Science and Data Analytics
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 credit-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
- 8 starts a year - Fall, Winter, Spring, and Summer
- Finish in as few as 18 months
- No GRE/GMAT required
Our faculty research information retrieval and data mining, combinatoric problems, query optimization, distributed shortest path algorithm, np-hard problems, and Big Data.
"Those individuals with skills and knowledge in the world of data science and data analytics have the ability to shape our worlds in so many ways. From what we buy, wear and eat to what we stream. Those that know data are going to be the kings and queens of this generation and the future."
Christine M. Wallace, Ph.D., M.Ed., Vice President, Kettering Global
Undergraduate degree with a background in the sciences, Data Science or Analytics, Mathematics, Engineering, Computer Science, or Business Analytics preferred, but not 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.
- #3 Best Value Colleges in Michigan - Niche - 2023
- #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 388 Colleges in the United States - Princeton Review - 2023
- Silver-Status University - Veteran-Friendly Schools (VFS) - Michigan Veterans Affairs Agency - 2022
About Data Science and Data Analytics
Data Science and Data Analytics are evolving, multi-faceted, interdisciplinary fields that seek to combine, refine, and develop methods and processes from statistics and computer science into modern scientific data analysis.
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. Data Analytics professionals use data to make complex decisions and gain insights invaluable to business, industry and service.
Career Outlook for Data Science and Data Analytics
Data scientists will experience a 15% increase in demand for their expertise, with significant growth in opportunities projected through 2029.
- More than half of data scientists have master’s degrees, a 20% increase since 2019.
- Careers in data science are some of the highest-paying and in-demand roles within the U.S. and beyond.
Data Professionals 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.
An online Master of Science in Applied Data Science and Data Analytics will make you stand out, offering a competitive advantage in your career.
Kettering University Online – Master of Science in Data Science FAQs
What Are the Admissions Requirements?
Domestic
MS Applied Data Science and Data Analytics Online - FAQs
- Undergraduate degree with 3.0 on a 4.0 grading system, or the international equivalent (85 overall grade point average on a 100-grade-point scale.) A 2.5-2.99 GPA will be considered on a provisional basis.
- A background in science, Data Science or Analytics, Mathematics, Physics, Engineering, or Computer Science is preferred, but not required
- Current or previous job experience will be considered. Those working in the fields of actuary science, the insurance industry, health care and business will be considered on a case-by-case basis
- Resume
- Statement of Purpose
- Three Professional Letters of Recommendation (one must be from a supervisor)
- Official transcripts from a regionally accredited U.S. university or an international equivalent
- Up to 8 hours of transfer credits may be available
- No GRE or GMAT is required
In addition to the Domestic requirements, International requirements also include:
- International students are required to submit educational documentation to an evaluation service such as Educational Perspectives, which is a member of National Association of Credential Evaluation Services (NACES). This will be at the expense of the student. Kettering University undergraduate students need not submit their Kettering transcripts but are required to submit transcripts from any other university.
- International applicants whose native language is not English and who have not earned a bachelor’s degree from a U.S. institution are required to take the Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS), MELAB (offered by University of Michigan), or complete level 112 at an approved ELS center. Please have official scores sent to Kettering University’s Office of Admissions, Code 1246. Photocopies will not be accepted.
Our minimum score requirements are:
- TOEFL: Paper-based: 550
- Computer-based: 213
- Internet-based: 79
- IELTS: Minimum Band score of 6.0
- MELAB: 76
Admissions
Our knowledgeable and supportive Admissions Advisors help you understand KUO's program structure, mission, core values, and goals. They help you understand how the program you are interested in pursuing aligns with your career goals and assist you in preparing a strong file for the Admissions Committee.
Student Support
Online students enrolled with KUO receive individual guidance from a Professional Advisor. KUO is committed to helping you thrive as an online student —that’s why we offer support from the start of your classes until graduation, ensuring you stay connected to the university.
MS Applied Data Science and Data Analytics Online - Curriculum
MS Applied Data Science and Data Analytics Online - Courses
*While other options for this program may be available on campus, the KUO program is as noted
Earning your Master of Science in Applied Data Science and Data Analytics from Kettering University Online, a global leader in STEM education, you learn from expert faculty and gain real-world skills. You graduate with the skills to lead and impact your organization, leveraging the power of data.
Connect with us — get your questions answered. Complete the form at the top of the page to download your program guide and schedule a time to speak with an Admissions Advisor.
This is a course on statistical methods for data science with an emphasis on statistical learning. It provides a set of tools for modeling and understanding big and complex data. This course concentrates on applications and practical execution of the methods rather than on mathematical details. Areas discussed include various regression models, classification methods, resampling, non-linear techniques, tree-based analysis, support vector machines, and unsupervised learning. Programming language R will be introduced and used throughout the course.
Visualizations are powerful. Theories of visual rhetoric and design teach us that good visualization is not only clear and accurate but appealing as well. When executed well, visualizations enhance oral or written communication, by supporting arguments and claims, by providing insight into complex issues, and by supporting recall and decision-making in audiences. This relationship goes both ways, however, even well-crafted visualizations must be supported by effective oral and written communication. In this course, students explore both sides of this relationship, becoming familiar with common genres of visualization and with techniques both for designing them effectively and ethically, and for presenting visualizations orally and in prose.
The concepts, principles, issues and techniques for big data and cloud computing are introduced in this course. This course will provide a foundation in data science based 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.
This course provides an introduction to machine learning. Topics include: supervised learning including generative, discriminative learning, parametric and non-parametric learning, neural networks, support vector machines; unsupervised learning including clustering, dimensionality reduction, kernel methods; learning theory bias/variance trade-offs; VC theory; large margins; reinforcement learning. The course will also include applications of machine learning to big data.
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, genetic algorithms, nearest neighbor method, and rule induction.
The rise of big data and machine learning has transformed the business world. In fact, these tectonic shifts in the business landscape are labeled as the fourth industrial revolution. Data is the new oil, creating enormous wealth and opportunity for businesses. This course will introduce the strategic importance and applications of these new Artificial Intelligence (AI) technologies. This is a hands-on learning course towards developing skills in using the Python language for data cleaning, exploration and modeling. The overarching aim is to provide a strong start towards developing skills that will eventually lead towards becoming an accomplished data scientist, who understands and is able to apply these skills towards achieving organizational competitive advantage.
Introduction to computer science concepts and basic programming skills that are specifically geared toward data science, and forms a part of the introductory coursework for a Data Science & Data Analytics degree. Course emphasizes writing programs that are capable of retrieving and manipulating large amounts of data. The first half of the course focuses on Python as a first programming language, while the second half of the course covers selected advanced topics such as data visualization, web scraping, database access and others.
This course is intended to develop student facility with a variety of quantitative techniques to facilitate the managerial decision-making process. Simulation approaches are covered along with optimization techniques such as linear programming and stochastic techniques such as queuing models. In this course, students will develop spreadsheet modeling skills, and emphasis will be placed on the application of these quantitative techniques to a variety of managerial areas.
Students receive an overview of effective strategies for managing supply chains as well as an introduction to operations within complex networks and logistics. Practical skills to increase service levels and reduce costs are examined. Additional areas of examination include the following: strategic planning and operation of an effective supply chain design, advantages of competitive supply chains and how weaknesses in the chain impact operations, key drivers of supply chain performance, application of analytical methodologies to impact demand planning in supply chains, and an overview of the use of technology in supply chain management.
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.