Kettering was chosen by students nationwide as the top university for career preparation in a survey conducted by The Wall Street Journal and Times Higher Education. Kettering continues to produce C-suite alumni in thousands of different business sectors and across disciplines.
Online Master of Science in Data Science
Change the World and Drive Innovation through Data Solutions
Kettering University’s Online Master of Science in Data Science a fusion of statistical, data management, and computing technologies—including data mining, machine learning, cloud computing, and visualization.
A Master of Science in Data Science from Kettering University’s Online program prepares leaders to manage and direct teams to create, define and refine data. Students will learn how to design and build new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis to achieve organizational goals.
Learn highly scalable statistical and computing techniques for processing and building statistical and data models, such as the ability to:
- Frame data science tasks in the context of organizational or project goals
- Effectively communicate data science concepts, results, and visualizations
- Use appropriate quantitative models to solve data science tasks, and assess models used to solve data science tasks
- Use powerful methods and technologies that unlock solutions hidden in data
- Apply well-rounded knowledge and professional skills to pursue leadership roles in data science
Program Eligibility
Undergraduate degree with a background in science, Data Science or Analytics, Mathematics, Physics, Engineering, or Computer Science preferred, but not required. Current or previous job experience considered. Those working in the fields of actuary science, the insurance industry, health care and business considered on a case-by-case basis.
- GRE waiver available for Kettering undergraduates or students with a minimum 3.0 GPA from an ABET undergraduate school
Who Should Apply
Ideal students should possess strong mathematical and statistical skills, a computer science background, related work experience, and the motivation to drive their career forward with advanced expertise in data science.
Fast Facts
- 100% online – ideal for working professionals
- Complete in as few as 24 months
- Four intakes per year (fall, winter, spring, summer)
- Accredited by the Higher Learning Commission and a member of the North Central Association of Colleges and Schools
- Kettering University ranked among the top 25 best regional universities in Midwest rankings for US News and World Report
- Kettering University was nationally ranked 24th in Return on Investment in PayScale.com’s 2019 report
MS Data Science Program
Kettering University’s Master of Science in Data Science is a fusion of statistical and computing technologies including data mining, machine learning, cloud computing, and visualization. It equips graduates with essential business tools and the ability to use them to create a competitive advantage in their industry. In a data-intensive domain, students will estimate the unknown by asking questions, creating and writing algorithms, and building statistical models.
Equipping students with the ability to collect, prepare, and refine data, this online master’s program prepares graduates to solve organizational problems with the power of data science.
Check out our University Rankings:
- Ranked 3rd among 2019 Best Value College in Michigan - Niche
- Named 24th nationally in Return on Investment (ROI) - PayScale.com
- Ranked among the top 100 2019 Best Colleges with No Application Fee in America - Niche
Data Science vs. Data Analytics
Information drives and influences industries across our global economy. Data allows decision-makers to understand what’s happening now (data analytics), as well as why it’s happening and how it will likely influence the future (data science).
When considering a data-oriented degree, your skills in math, computer science, and advanced problem-solving can factor into which path to take. Data Science is a rigorous field that’s STEM-oriented and requires an advanced skill set to creating, defining and refining data in order to build new processes for data modeling and production. It utilizes prototypes, algorithms, predictive models, custom analysis, and a substantial amount of coding to anticipate the unknown and solve problems before they happen. Careers in data science are some of the highest-paying and highest-demanded roles within the U.S. and beyond.
Data Analytics, on the other hand, is a field with a lower barrier to entry that involves the process of examining and interpreting large data sets. It’s suited for individuals who would like to apply more sophisticated analytics methods to their functional role (in fields such as marketing, finance, human resources, and IT). These roles experience a significant demand as well but are less math and technology-intensive and have a lower salary potential compared to data science careers.
If you’re ready to change the world with data, a Master of Science in Data Science from Kettering University’s Online program is the perfect way to start.
We are Here to Help
We understand pursuing a master’s degree in data science can be a big decision with many factors to consider. Our knowledgeable and supportive enrollment advisors are here to help guide your decision about our program offerings to achieve your career goals.
By connecting with an Enrollment Advisor, you can expect to:
- Receive in-depth knowledge on the program and the admissions process
- Benefit from having a 1-on-1 conversation to ensure the program fits your academic and professional goals
- Learn more about Kettering University’s mission, core values, and goals, and our support systems for online learners
- Be advised and assisted with putting together a strong file for the Admissions Committee to review
MS Data Science Program
*While other options for this program may be available on campus, the KUO program is as noted
Set yourself apart with a Master of Science in Data Science from Kettering University. The 40-credit MS Data Science curriculum consists of ten courses, including a capstone course.
Online MS Data Science Career Outlook
Skilled data scientists are in high demand across virtually all industries. As businesses and organizations generate large sets of data and seek ways to leverage it for decision-making and technology development, the career outlook for data science professionals is bright.
The median base salary for data scientists is $130,000. – Tech Republic
Data scientist jobs are projected to have a 37% annual growth rate. – LinkedIn, 2020 Emerging Jobs Report
Job satisfaction, salary, and growth have led to data scientists topping Glassdoor’s “Best Jobs in America” from 2016 through 2019. – Glassdoor.com
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.
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