Online Master of Science in Data Science

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; Mathematics, Physics, Engineering, or Computer Science preferred
  • Preferred prerequisite coursework includes:
  1. Calculus I and Calculus II
  2. Probability & Statistics
  3. Linear Algebra
  4. Intro to Computer Science
  • GRE waiver available for Kettering undergraduates or students with a minimum 3.0 GPA from an ABET undergraduate school
  • Work experience in fields requiring strong analytical/statistical skills preferred (e.g. Actuary scientist, epidemiologist, etc.)

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

Download the free guide

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 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

Schedule an Appointment

MS Data Science Courses

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 that include 9 courses, and your choice of either a research project, internship, or creating a capstone data science project from start to finish.

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

CS 641 | FOUNDATIONS OF DATA SCIENCE
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.
CS 690 | CAPSTONE PROJECT IN DATA SCIENCE
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.
COMM 601 | COMMUNICATING DATA
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.
CS 665 | DATA MINING AND INFORMATION RETRIEVAL
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.
MATH 627 | PROBABILITY AND STOCHASTIC MODELING
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.
MATH 637 | STATISTICAL INFERENCE AND MODELING
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.
CS 651 | CLOUD COMPUTING - ARCHITECTURE & APPLICATIONS
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.
CS 661 | DATABASE SYSTEMS
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.
CS 682 | MACHINE LEARNING
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.
CS 691 | SPECIAL TOPICS IN DATA SCIENCE
Current topics in Data Science are discussed and analyzed.