Data has a story. Be the one to tell it with Butler’s online data analytics master’s degree.


Butler’s online master’s in data analytics program empowers students with the analytical skills required to create and deploy data-based actionable insights to improve decision-making in their organization.

The online data analytics degree program offers two concentrations: Business Analytics and Healthcare Analytics. No matter which path you choose, Butler’s approach to analytics will give you the knowledge and skills you need to excel in today’s data-centric world. Through our program, you will:

  • Learn from industry professionals with extensive knowledge of data principles.
  • Interpret data and offer meaningful, ethically-minded insights and visually compelling results.
  • Hone your critical thinking while mastering big data through a cross-functional learning approach.
  • Study machine learning, data mining, and predictive modeling while advancing your career with a flexible online format.





  • GRE/GMAT Not Required
  • Healthcare Analytics Concentration: 31-Credit Hours
  • Business Analytics Concentration: 32-Credit Hours
  • 11 Courses
  • 100% Online With Weekly Live Instruction
  • Scholarships Available

“What initially caught my eye was that it was a program for working adults that builds from the ground up. However, when I got to talk to some of the professors involved in the creation of the program, their passion for the field and for the students made Butler my first choice.”

Julia B., MS in Data Analytics, 2023
Data Engineer at TriHealth
Cincinnati, OH

Master's in data analytics student Julia Brooks

Learn how our MS in Data Science faculty teach the technical and soft skills you need to craft impactful data insights. Through collaborative coursework and community partnerships, we prioritize your individual growth as a data-driven professional.

Harness the Power of Data and Boost Your Earning Potential.

Interpret data, draw insights, and deliver actionable results with a master’s in data analytics.


Recruiters seek out professionals with a master’s in data analytics more than any other business degree type.* As a result, career opportunities across industries continue to grow for data analytics master’s degree holders and the median starting salary has increased to $105,000.*

*Graduate Management Admission Council, 2022


Earning an MS in Data Analytics may position you for opportunities in the following career paths:

Job Title
Employed (2021)
Median Income (2021)
Job Title General and Operations Manager
Employed (2021) 2,985,000
Median Income (2021) $115,000
Job Title Information Security Analyst
Employed (2021) 163,000
Median Income (2021) $104,000
Job Title Database Administrator or Architect
Employed (2021) 145,000
Median Income (2021) $101,000
Job Title Computer Systems Analyst
Employed (2021) 539,000
Median Income (2021) $99,000
Job Title Statistician (includes mathematicians)
Employed (2021) 36,000
Median Income (2021) $96,000
Job Title Management Analyst
Employed (2021) 951,000
Median Income (2021) $93,000
Job Title Operations Research Analyst
Employed (2021) 104,000
Median Income (2021) $82,000

Source: U.S. Bureau of Labor Statistics, 2021. All figures rounded to the nearest thousand.


The MS in Data Analytics program consists of two foundational skills courses, five core courses, and four concentration courses. All programs feature a flexible online model where students take one seven-week course at a time.


This course serves as the introduction to the Data Analytics master’s program. Its purpose is to introduce to students R and R Studio as a data analytic platform. Students will learn the basics of R by using it to review and learn basic statistical analyses.

Prerequisite: Admission to degree and/or certificate programs or permission from the Program Director.

This online course is an introduction to analytical programming in Python and Data Management using SQL. The course presents foundational material required for the Data Analytic MS Core Courses to ensure students are ready for that material. Some of the course material will be similar to that offered in a computer science course but will focus on the skills most often required by analysts rather than programming and database specialists.

Prerequisite: Admission to degree and/or certificate programs or permission from the Program Director.


This course provides an experiential introduction to current issues and methods in data mining, applications of some introductory data mining algorithms. A leading statistical and data mining software package, R will be used to apply techniques learned in the class to some modern and classic data sets. Topics include algorithms for supervised and unsupervised machine learning techniques.

Prerequisites: DATA600 Data Analysis using R; DATA604 Python Programming and Data Management.

The ability to communicate the results of data analyses is as important as the analyses themselves. This course will introduce topics important to data storytelling, to allow students to become better data presenters and critical viewers of data. Various visualization tools will be presented, and students will learn how best to report data to various types of audiences. Students will gain experience producing interactive, automatically updating data “dashboards.” Students will collaborate to produce collective visual stories, and provide critical reviews of each other’s work. Relevant areas of data ethics will also be introduced, including the protection of customer or patient privacy and the importance of conveying the uncertainty of results.

Prerequisites: DATA600 Data Analysis using R; DATA604 Python Programming and Data Management.

This course will provide an introduction to the advanced analytics and data mining models using health care datasets. The models taught will include different instances of generalized linear regression model, mixture models, time series models (AR, ARMA, ARIMA, TAR, Change Point). Software packages such as R and SAS will be used throughout the course.

Prerequisites: DATA600 Data Analysis using R, DATA604 Python Programming and Data Management; DATA610 Introduction to Data Mining Core course.

This course provides an experiential overview of current issues in data analytics from both analytic and computer sciences perspectives. The focus is on learning techniques for 1) scraping, cleaning, and manipulation of raw data from a variety of live sources and preparing them for analysis, 2) learning to manipulate and reorganize data to apply a variety of analytic tools, and 3) manipulate a variety of data features to enhance to predictive power of statistical models. The focus of this course will be primarily on techniques and tools used to extract data from various sources (primarily live and active data streams), prepare it for analysis, and then apply those analyses with a special emphasis on the understanding of feature engineering: the process of creating representations of data that increase the effectiveness of a predictive model.

Prerequisites: DATA600 Data Analysis using R, DATA604 Python Programming and Data Management; DATA610 Introduction to Data Mining Core courses.

This course will utilize Python to develop text and image processing techniques. Students will leverage industry standard methodologies for text and image processing. Students will gain experience with feature extraction and classification techniques in both visual and textual domains using neural networks and traditional machine learning models. This is a 3-credit hour project-based course.

Prerequisites: DATA604 Python Programming and Data Management; DATA612 Visualization, Storytelling and Ethics


This course will focus on healthcare data governance, management, and ethics. The course will explore the techniques involved with healthcare data capture, cleaning, storage, and security and examine methods to overcome challenges of managing healthcare data across multiple systems. A critical review of the ethical considerations of healthcare algorithm utilization will be conducted.

Prerequisite: None

The purpose of this course is to expose students to the health outcomes research and help them prepare for non-traditional career options, including pharmaceutical industry, managed care, or fellowships. It would entail learning to conduct research in the field of health outcomes and design a research study in a specific therapeutic area or condition. Students are encouraged to select a therapeutic area of interest before the class begins. They can work in groups or individually for the research projects, based on their preferences. They would be required to use SAS Enterprise Guide to conduct the statistical analysis. Overall, the class will help students learn & apply research methodology and statistics to the specific therapeutic area or condition of interest, from the health outcomes perspective.

Prerequisites: None

This course examines the current and future states of healthcare data and explores methods to leverage analytics to optimize healthcare outcomes and value. Students will learn standard healthcare terminologies and relational databases. Students will utilize SQL for analytic applications.

Prerequisite: DATA604 Python Programming and Data Management

In this course, students will collaborate with an institutional partner to apply healthcare analytics principles and techniques to a longitudinal project of mutual interest. Students may choose from a variety of focus areas including hospital and health systems, local health initiatives, technology and innovation, healthcare educational institutions, and industry. This course may be on campus, off-campus, hybrid, or online pending the nature of the scholarly project. The student will submit a project that successfully meets the course outcomes and is approved by the subject matter expert and Program Director.

Prerequisite: DATA 620 Utilization of Health Data; DATA622 Healthcare Data Literacy and Analytics; DATA624 Statistics and Research Methods for Healthcare Analytics.


This course covers how organizations can use analytics to gather and utilize information to evaluate risk, increase profitability, and generally improve business performance. The material emphasizes data-driven managerial decision making in a structured approach to problem solving. Topics include an introduction to big data, viewing data and analytics capabilities as strategic assets, and employing analytical models to solve problems.

Prerequisite: DATA604

This course reviews the statistical processes and analytical tools marketing managers may need and could employ in order to make decisions regarding marketing functions such as segmentation, targeting, positioning, among other functions. Further, this course reviews the best analytical processes marketing managers may use to evaluate customer lifetime values, customer buying behaviors, international market analyses, and digital marketing effectiveness. The statistical analyses covered include, but are not limited to cluster analyses, descriptive analyses, analyses of variance, regressions, structural equation modeling, and multilevel analyses.

Prerequisite: DATA604

This class covers the analysis of data related to accounting professionals. The focuses include analytic techniques for decision making and the examination of “big data” involving accounting information. Data analytics has become a relevant skill for all business managers and particularly accountants who often know both internal and external data better than anyone inside the organization.

Prerequisite: DATA604

As the last course in the MS in Analytics program, students will have the opportunity to leverage the learning from all core classes and apply their skills in a real-world project. Supply chains are dynamic and complex environments that involve making decisions on different levels and demand the use of real data for real business problems. A set of different analytics tools will be used to collect, organize, and analyze data that support key supply chain decisions, as well as the different challenges and opportunities they pose.

Prerequisites: DATA640, DATA642, DATA644



Per credit (31-32 Credits)
*Tuition is subject to change each academic year.


Application Fee**
**Waiver available. Contact your enrollment advisor at 317-324-1305 or OnlineMSDA@butler.edu.


Enrollment Deposit

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When you choose Butler University, you choose an experiential, student-centered education. Ranked as the No. 1 Most Innovative School in the Midwest for eight consecutive years, we continually implement new teaching strategies, foster strategic community partnerships, and invest in tools and techniques to help our students to become strategic thinkers and leaders.

Butler’s 21-month online MS in Data Analytics program will prepare you to tell the story behind data. Through live, synchronous courses, our industry-leading faculty will help you hone your soft skills and build your data analytics skill set. 

You’ll also be able to customize your studies to your interests and career goals when you choose a Business Analytics or Healthcare Analytics concentration. With Butler’s flexible, online master’s in data analytics program, you will graduate ready to drive decision-making and innovation in a data-centric world. 


We require all applicants to hold a bachelor’s degree with a minimum GPA of 3.0 from an accredited institution or the international equivalent.

No, GRE scores are not required.

All non-US citizens are required to provide a copy of their passports, official evaluated transcripts (you are not required to submit a credential evaluation up front, but this may be requested at a later date at Butler’s discretion), and an English language proficiency exam score (TOEFL, IELTS, or Duolingo). Review the full list of English proficiency requirements.

No. International applicants do not have to submit evidence of financial support.

No. Butler’s online MS in Data Analytics program does not provide an I-20 to be used for an immigration visa. This degree does not provide an extension or continuation of a current visa status.

Butler offers multiple financial assistance options to help fund your education. The Office of Financial Aid awards Federal Direct Loans to degree-seeking graduate students enrolled at least half-time (three credit hours. You may also consider private loans after exhausting federal aid options. Take time to review our tuition and fees and create your financial plan. The Office of Student Accounts can provide additional information about billing and payment plans.

Butler’s data analytics master’s program offers concentrations in Healthcare Analytics (31 credit hours) and Business Analytics (32 credit hours). Both pathways consist of 11 courses.

On average, students in the online master’s in data analytics program dedicate 15-20 hours per week to their coursework. Explore these seven key tips  from data analytics master’s students to prepare for the online learning experience. 

Butler’s online programs are designed with the same high-quality curriculum and taught by the same distinguished faculty as our on-campus programs. You’ll learn from industry experts and engage with your peers in small classes through a flexible, online format designed to work alongside your busy schedule.

We know that students need real-world practice to thrive as professionals upon graduating and moving into the workforce. That’s why we’ve built hands-on experience into our online programs in the form of live debates, presentations, and breakout group discussions with fellow graduate students. The online learning experience combines innovative technology, a carefully considered curriculum, and active partnerships that fully prepare students to be the world-changing professionals they set out to be.

Canvas Learning Platform

  • Our learning management system, Canvas, serves as students’ centralized hub for all course content and activities—think of Canvas as your college campus. Our students use this system to manage everything from asynchronous course content, course syllabi, assignments, and communication with instructors and peers.

Class Structure and Feel

  • Includes live debates, presentations, and breakout group discussions with peers, emphasizing group work, collaboration, and a feeling of intimacy within our online classrooms. Students complete coursework, assignments, and readings prior to class and are ready to use learned information in lively classroom discussions. This “flip model” approach to learning allows students to get more out of the live classroom experience.

Courses in Butler’s online data analytics degree program consist of asynchronous coursework and live synchronous classes via Zoom. Class sessions are typically held on Tuesday, Wednesday, or Thursday evenings and run for 60-120 minutes. Classes start as early as 5:00 PM ET and as late as 7:30 PM ET.

One of the most valuable aspects of our program is the live component that allows you to interact and collaborate with your peers and faculty members through Zoom. Courses in the data analytics master’s program are instructed in Eastern Time (ET). When applying, please consider the time difference between your state or country and Butler University’s time zone to ensure your academic success in the program.

Butler University offers three starts per year for this program—spring (January), summer (May), and fall (August).

The data analytics field is growing quickly in the United States. You can follow many career paths with a data analytics master’s and earning this degree can increase your earning potential. Explore these common questions about careers in data analytics to determine if the Butler online master’s in data analytics program is right for you.

Data analysts use techniques from statistics, business intelligence, and information science to collect, organize, and analyze small sets of data. They usually create visualizations and reports for stakeholders to communicate their findings.

Data scientists possess the capabilities of data analysts, and additionally, they work with more complex, larger sets of unstructured data. Data scientists use the same techniques for analysis, but also employ software engineering, machine learning, mathematics, and computer science. While there are distinctions between the two fields, lines are becoming increasingly blurred as data analytics programs continue to expand the data types and techniques they teach.


Butler University is accredited by the Higher Learning Commission.



Bachelor’s degree required.

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