Online

Master of Science in Data Analytics

Unlock the Power of Data.

Butler’s online Master’s in Data Analytics program equips students with the necessary analytical skills to generate data-driven insights for improved decision-making in their organizations. The program offers students with the skills they need to excel in today’s data-centric world. Students will learn from industry professionals, gain proficiency in data interpretation, ethics, and visualization, and develop critical thinking skills through a flexible online format that covers machine learning, data mining, and predictive modeling.

Apply Request Info

Explore This Section

1 Program Details
2 Program Highlights
3 Career Opportunities
4 Admission & Tuition
5 FAQs

Program Details

Requirements

The admissions requirements for the Master’s in Data Analytics program encompass a comprehensive evaluation of candidates’ qualifications and experiences.

Degrees

GRE/GMAT (Not Required)

Unlike many other graduate programs, Butler University does not require applicants to submit GRE, GMAT, or any other entrance exams as part of the admissions process. Instead, the university focuses on a holistic evaluation of candidates, considering their academic records, professional achievements, letters of recommendation, and personal statements. This policy ensures that individuals with various career trajectories and educational backgrounds have an equal opportunity to pursue a graduate degree at Butler, promoting a more inclusive and diverse cohort of students.

Personal Statement

Applicants for Master’s in Data Analytics program are required to submit a personal statement as a component of their application. This personal statement serves as a valuable opportunity for candidates to showcase their motivations, goals, and unique qualifications that make them suitable candidates for the program. Applicants are encouraged to articulate their career aspirations, their reasons for pursuing a graduate degree at Butler University, and how their past experiences and achievements have prepared them for success in the program.

Letters of Recommendation

As part of the admission requirements for Master’s in Data Analytics program, applicants are asked to provide two letters of recommendation. These letters play a significant role in assessing an applicant’s qualifications and potential for success in the program. Candidates should select individuals who are well-acquainted with their professional and academic abilities and who can provide insight into their character, work ethic, and potential for leadership. These letters of recommendation are a crucial aspect of the application process, as they offer valuable insights and endorsements from individuals who have observed the applicant’s capabilities firsthand, helping the admissions committee make well-informed decisions about each candidate’s suitability for the Data Analytics program.

Transcript and Resume

Curriculum

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.

Foundational Skills

DATA600 DATA ANALYSIS USING R (GRADUATE, 2 CREDIT HOURS)

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.

DATA604 PYTHON PROGRAMMING AND DATA MANAGEMENT (GRADUATE, 2 CREDIT HOURS)

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.

Core

DATA610 INTRODUCTION TO DATA MINING (GRADUATE, 3 CREDIT HOURS)

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.

DATA612 VISUALIZATION, STORYTELLING AND ETHICS (GRADUATE, 3 CREDIT HOURS)

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.

DATA614 ADVANCED ANALYTICS, PREDICTIVE MODELING, AND DECISION MAKING (GRADUATE, 3 CREDIT HOURS)

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.

DATA616 DATA ENGINEERING, CURATION, AND ANALYTICS (GRADUATE, 3 CREDIT HOURS)

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.

DATA618 TEXT AND IMAGE PROCESSING TECHNIQUES (GRADUATE, 3 CREDIT HOURS)

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

Concentration

Concentrations

Currently there are one concentrations offered as part of the Master’s in Data Analytics program: Business Analytics.

Business Analytics

DATA640 INTRODUCTION TO BUSINESS ANALYTICS (GRADUATE, 3 CREDIT HOURS)

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

DATA642 MARKETING ANALYTICS (GRADUATE, 3 CREDIT HOURS)

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

DATA644 DATA ANALYTICS FOR ACCOUNTING (GRADUATE, 3 CREDIT HOURS)

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

DATA648 SUPPLY CHAIN ANALYTICS (GRADUATE, 4 CREDIT HOURS)

As the last course in the MS in Data 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

Accreditation

Butler University is accredited by the Higher Learning Commission.

Contact information for Higher Learning Commission:

30 N. LaSalle Street, Suite 2400, Chicago, IL 60602
800-621-7440
Higher Learning Commission website

Statement of Affiliation Status

Program Highlights

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.

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.

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, ’23
Data Engineer
TriHealth