online master's degree of data science
Summer term classes start in June.
100% online learning.
Earn your degree on your schedule
$680 per credit hour.
30 total credit hours.
get hands-on experience while earning your master's in data science.
All industries collect vast amounts of data about their customers, operations, and financial performance, and need professionals to guide them in how to use this data. Bellevue University’s master’s in data science prepares you with the tools and knowledge to curate, analyze, and make relevant discoveries from large data sets. Through experienced faculty and practical coursework, the MS in data science program will expose you to leading data science tools such as Python, R, Tableau, and PowerBl, as well as develop skills such as data analysis and visualization and how to apply such data to business problems.
Our online data science degree is flexible for busy working adults.
Bellevue University offers a completely online data science master’s program with a flexible schedule that allows you to work in your career while obtaining your degree. Faculty are highly available through multiple channels, including email and chat.
Why get a master's in data science?
You can become a leader in data analysis with a master’s in data science. Employers around the world are inundated with massive amounts of data and need skilled employees to glean insights from that data. They are seeking qualified data scientists who know how to prepare datasets for solving problems to guide better decision-making and strategic business moves. The master’s in data science qualifies you to recommend appropriate, ethical data modeling techniques to test hypotheses. You’ll be able to communicate data science results and transform them into answers for business queries and challenges. Data science experts are a major asset to any industry and sought after in many business fields.
what you'll learn.
In this program, you’ll learn to collect, analyze, and interpret data to solve real-world problems while applying ethical practices in every stage of analysis.
upon graduation, students will be able to:
- Prepare datasets for solving problems.
- Recommend appropriate data modeling techniques to test hypotheses.
- Communicate data science results into answers for domain challenges.
- Identify ethical considerations in dataset preparation and modeling.
Awards
Bellevue Stories
Data Science degree courses
Current students please login to BRUIN and select “Academic Progress” for your curriculum requirements.
Requirements (30 credit hours)
This course introduces the possibilities, history, and ethics surrounding Data Science. Basics of data science are explored, including vocabulary, programming languages, big data frameworks, visualization, and statistics. Prior programming experience is not needed for this course.
This course introduces the Python programming language as a tool to clean, slice, and build tools to analyze an existing dataset. Basic principles of programming are explored as well as techniques for configuring a computer for data science work. Prerequisite: Recommend DSC 500
The R programming language and software environment is commonly used to explore all types of data. Using R, students perform statistical tests on the data. Report writing and presentation of data are introduced. Prerequisite: Recommend DSC 500
This course introduces complex techniques needed for profiling and exploring data. Students use programming and statistics-based inference to ask and answer insightful questions of data. Prerequisite: Recommend DSC 510 and DSC 520
Much like life, the data humans produce is infinitely variable in its structure, presentation, and scale. This course prepares students for this infinite variety of data. Students use Python, SQL, and other tools to acquire, prepare, clean, and automate dataset creation. Prerequisite: DSC 510 or equivalent and recommend DSC 530
Data can often contain patterns and anomalies that only emerge at large scale. In this course, you will import, clean, manipulate, visualize, analyze, and model structured and unstructured data to extract this information. Model building topics covered include text sentiment analysis, regression, classification, and neural networks. Furthermore, you will learn how to perform feature dimensionality reduction and tune model hyperparameters. The knowledge learned in this course culminates in a term project. Prerequisite: Recommend DSC 540
This course assembles topics covered in previous courses into an applied project. Students have the opportunity to find, clean, analyze, and report on a project they define. Advanced methods of analysis using Python and R allow students to delve deeper into their projects. Prerequisite: DSC 540 or equivalent and recommend DSC 550
Data scientists should be great storytellers, whether using visual, text, or other means. In this course, students explore the basic storytelling components of data science and apply them to different types of data for different types of clients and audiences. Presentation techniques, language use for different audiences, and visualization tools techniques are included. Prerequisite: Recommend DSC 630
In the final course of the Data Science program, students will conduct several data science projects from origin to presentation. Students will gather data, then prepare, clean, analyze, and present their analysis to an audience. Prerequisite: Completion of all other required DSC courses
Note: Students who have successfully completed CIS 309 with a minimum grade of “B” may not also receive credit for CIS 535. These students must take DSC 650.
This course covers the fundamentals of data infrastructure and how technologies fit together to form a process, or pipeline, to refine data into usable datasets. This course focuses on building a predictive modeling pipeline used by the various types of projects that are called, “big data.” Prerequisite: Recommend DSC 540
The major focus of this course will be the relational, dimensional and NoSQL models. Topics include relational and dimensional modeling, business intelligence, NoSQL databases and their application, SQL, application development using databases and emerging trends. Students will prepare a small application using a commercial database management system.
Generative Artificial Intelligence (GAI) is arguably one of the most transformative developments in information technology history. With uses of GAI ranging from creating essays to generating entire videos, this technology affects every industry, directly or indirectly. This course prepares students for GAI by showing how to apply it, delving into how large language models (LLMs) can work with text, and how images can be created and manipulated using GAI. Students will also explore prompt engineering, retrieval augmented generation, and model turning, which is how GAI is used, grounded in truth, and altered for specific uses. Finally, the course teaches students how to deploy custom LLMs, and how to architect and build applications around LLMs. Prerequisites: DSC 630 Predictive Analytic
Major Requirements Credits
= 30 total credits*
University Accreditation
Bellevue University is accredited by the Higher Learning Commission ( hlcommission.org ), a regional accreditation agency recognized by the U.S. Department of Education.
Whether a college, university, or program is accredited is important to students receiving financial aid, employers who provide tuition assistance, donors, and the federal government.
This program is considered a non-licensure degree/certificate program and is not intended for those seeking licensure or the practice of licensed profession. This program may be relevant to multiple occupations that do not require licensure and was not designed to meet educational requirements for any specific professional license or certification.
*Consult with an admissions counselor to determine your eligible credits, as well as to verify minimum graduation requirements for this degree. Transfer credits must be from a regionally accredited college or university. Bellevue University makes no promises to prospective students regarding the acceptance of credit awarded by examination, credit for prior learning, or credit for transfer until an evaluation has been conducted.
learn on your own time, from anywhere.
Flexible schedule.
Study on your own time with courses designed to fit your busy life—whether you're working, raising a family, or serving in the military.
Reliable technical support.
Access 24/7 tech support to keep you connected and focused on learning, no matter where you are.
Dedicated online student support.
From coursework access and connectivity issues to tutoring and resume assistance, we've got you covered.
Engaging online learning.
Enjoy interactive courses designed for real-world application, with multimedia content, discussions, and hands-on projects.
grow with faculty who’ve been where you are.