Master of Science in Data Analytics

All applicants to the Master’s in Data Analytics program must possess a baccalaureate degree from an accredited institution or the international equivalent prior to enrollment at the graduate level.

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.

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.

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.

To complete their application for the Master’s in Data Analytics program, prospective candidates are required to provide official transcripts from all prior educational institutions and submit a current resume. These documents are essential components of the admissions process, as they offer valuable insights into an applicant’s academic background and professional experiences

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.

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

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