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Master of Science in Data Analytics

Data Has a Story. Be the One to Tell It.

Program Overview

Butler’s Master of Science in Data Analytics program empowers students with the analytical skills required to create and deploy data-based actionable insights which improve decision-making in their organization.

Students learn technical skills, like predictive analytics and effective visualization techniques, essential for understanding complex datasets. Beyond the technical, Butler’s interdisciplinary program prioritizes soft skills, like critical reasoning and ethical decision-making, necessary to craft compelling stories with data. The Master of Science degree in Data Analytics is offered in three different concentrations: Business Analytics, Healthcare Analytics, and Bioinformatics. No matter the path, Butler’s holistic approach to data analytics ensures students leave the program with the knowledge and skills they need to excel in today’s data-centric world.

  • Learn from data analytics experts and gain real-world connections.

  • Employ key technologies in data analytics including data mining, machine learning, visualization, and predictive modeling.

  • Learn to strategically assess decisions and to appreciate the ethical implications of relying on data-driven algorithmic decisions.

  • Become versed in storytelling as a technique for communicating analyses and interpretations, and in presenting visually compelling results to diverse audiences.

Courses

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 Masters and Certificate Programs. 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 R and Python to develop text and image processing techniques. Students will learn and apply techniques to social media analytics and text processing projects. This 3-credit course is delivered entirely online.

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

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.

A course exploring the application of biotechnology in the treatment of human disease. Topics introduced include gene editing, cloning, and expression; recombinant proteins; canonical and next-generation DNA sequencing; pharmacogenetics and epigenetics; introduction to genomics, transcriptomics, and proteomics; oligonucleotide drug lead development; gene therapies; and monoclonal antibody-based therapies.

Prerequisite: None

The module begins with an overview of key biological concepts of genes, how genes are translated into proteins, and how protein structure is linked to biological function. The different biological databases are introduced, and the process of access, retrieval, and analysis of various biological databases are described. There will be relevant application problems that will analyze genomic, transcriptomic, and protein structure datasets. The module will introduce the different methods of sequence analysis, the building of phylogenetic trees, and evolution. Various current sequencing methods will be described, and applications for the study of this data will be carried out, including next-generation sequencing approaches. The module will also introduce basic principles of protein structure, their classification, and methods used for structure prediction and drug discovery.

Prerequisites: DATA630 Genomics & Biotechnology.

As the amount of biological data in the world continues to rise, the ability to process, analyze, and integrate this data has become critical to advancing our understanding of human health and disease. This course will introduce Next-Generation Sequencing (NGS) data and other high-dimensional data types, ranging from epigenomic, genomic, transcriptomic, proteomic, and metabolomic datasets. After demonstrating standard data processing techniques for different ‘omics types, we will introduce various statistical and machine learning methods for the analysis of the resulting quantitative data, including dimension reduction, differential expression, and pathway analysis. Finally, we will explore different ways to integrate different ‘omics data sets, along with pharmacogenomics and clinical data, to gain a greater understanding of important biological mechanisms. Students will practice with applications on publicly available, published high-dimensional data sets to identify optimal methods based on the data type and scientific objective.

Prerequisites: DATA630 Genomics & Biotechnology.

In this course, students will work on projects that use next-generation sequencing technologies. In consultation with the faculty and/or industry advisors, students will identify a problem in precision (personalized) medicine, as well as the overarching theme of translational bioinformatics that can be used to identify markers for drug effectiveness in patients and or alternative uses for drugs. As part of their project, students will evaluate and identify reputable genomic and pharmacogenomic websites that are used in precision medicine. Students will develop a project using actual examples of specific pharmacogenomic markers for drugs that are currently used in the clinic. In their analysis, students will learn how these data are used to develop new drugs and identify alternate clinical uses of existing treatments. During their project, students will consider ethics in clinical testing and genetic data dissemination. The principles addressed in this course will prepare the student to apply genomic tools and machine learning analysis which will explore an area of interest. The nature of the student project will determine if off-campus, hybrid, or online.

Prerequisites: DATA630 Genomics & Biotechnology; DATA630 Genomics & Biotechnology; DATA632 Bioinformatics and Precision Medicine; DATA634 Next Generation Sequencing and High-Dimensional Data Analysis.

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

Tuition Costs

Per Credit
$950*
*Tuition is subject to change each academic year.
Application Fee
$35
Enrollment Deposit
$500

Career Outlook

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

Job Title

Employed (2019)

Average Salary (2019)

General and Operations Managers

309,830

$103,210

Computer Operations

91,652

$91,853

Database Administrators and Architects

38,791

$98,738

Information Security Analysts

37,370

$103,584

Operations Research Analysts

33,183

$86,174

Statisticians

21,171

$91,978

Management Analysts

724,000

$85,260

Source: Emsi Labor Market Data and Bureau of Labor Statistics (BLS), 2020

Important Dates

Fall 2022

May 30, 2022

Early Decision Deadline

June 27, 2022

Priority Deadline

July 25, 2022

Final Submit Deadline

August 24, 2022

First Day of Classes

FAQ

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, a GRE is not required.

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.

 

Learning Platform: Canvas

  • Our learning management system (LMS), 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.

Review our tuition and fees and create a financial plan; there are multiple payment options and educational loans available. The Office of Financial Aid awards Federal Direct Loans to degree-seeking graduate students enrolled at least half-time (three credit hours). You can contact the Office of Student Accounts for information about billing and payment plans.

Butler’s MSDA offers concentrations in Healthcare Analytics (31 credit hours), Business Analytics (31 credit hours), and Bioinformatics (32 credit hours). All three pathways consist of 11 courses. 

On average, students in this program dedicate 15-20 hours per week to their coursework.

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.

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

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