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CPT Eligible

The Master of Science in Analytics has three concentrations: Healthcare Informatics, Pharmaceutical Sciences and Individualized.

Data analysts are forging new relationships in virtually every discipline: business, healthcare, geology, mathematics and statistics, biology, chemistry, computer science, information systems and technology, engineering, psychology, behavioral science, operations research, and more, in addition to potential interactions between these disciplines, using role-based interaction with information and analytics to enable highly- collaborative, data-driven organizations. The graduate of this program enters the workforce prepared for the complex, information-intensive world.

Program Goals

Graduates of the Master of Science in Analytics program will be able to:

Graduates become data scientists and analysts in finance, marketing, operations, and business intelligence working groups that generate and consume large amounts of data.

Program Concentrations

Program Lead

 Kevin  Purcell, Ph.D.

Kevin Purcell, Ph.D. Program Lead for Data Analytics and Associate Professor of Data Science

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Full Time Faculty

Srikar Bellur, Ph.D.

Assistant Professor of Data Analytics

Wei-Kang Kao

Assistant Professor of Statistics and Analytics

Andre L’Huillier

Assistant Professor of Computational Social Science

Stephen Penn, DM, PMP

Associate Professor of Business Analytics & MEBA Program Lead

Kevin Purcell, Ph.D.

Program Lead for Data Analytics and Associate Professor of Data Science

Corporate Faculty

Benjamin Cohen

Corporate Faculty (Analytics)

Ian Davis

Corporate Faculty (Analytics)

Alan Hitch

Corporate Faculty (Analytics)

Matthew Hubley

Corporate Faculty (Analytics)

Joanna Karet

Corporate Faculty (Analytics)

Christina Ma

Corporate Faculty (Analytics)

Edmund Maher

Corporate Faculty (Analytics)

Amalie McKee

Corporate Faculty (Analytics)

Arnold Miles

Corporate Faculty (Analytics)

Ali Motamedi

Corporate Faculty (Analytics)

Martin Negron

Corporate Faculty (Analytics)

Mark Newman

Corporate Faculty (Analytics)

Divya Rao

Corporate Faculty (Analytics)

Edison Rolle

Corporate Faculty (Analytics)

Erika Wilson

Corporate Faculty (Analytics)

Zi Yang

Corporate Faculty (Analytics)

Program Courses

This program requires a total of 36 semester hours: 15 semester hours from the core courses, 6 semester hours of experiential courses, and 15 semester hours of Concentration courses. The semester hour value of each course appears in parentheses ( ).

ANLY 500 – Analytics I: Prin & Applicatiions (3 credits)

The first course in analytics covers the core concepts and applications of analytics. The student is introduced to the main concepts and tools of analytics including descriptive, predictive, and prescriptive analytics. During the course, the student uses a variety of statistical and quantitative methods, computational tools, and predictive models to make data-driven decisions. By the end of the course, the student will apply the concepts to real work projects where, by asking some questions about an issue or situation, use analytical tools to respond to it, and present it to technical and layperson audiences.

Prerequisites: MATH 220, MATH 280
Corequisites: ANLY 502

ANLY 502 – Analytical Methods I (3 credits)

This course reviews the fundamental mathematics required to be successful in the analytics program. It is designed to strengthen the mathematical abilities while addressing the requirements for coding/scripting. It presents the mathematical topics as coding/scripting problems.This is intended to further strengthen the ability to develop the subroutines/codes/scripts that are also necessary in an analytics career.

Prerequisites: None
Corequisites: None

ANLY 506 – Exploratory Data Analysis (3 credits)

Exploratory data analysis plays a crucial role in the initial stages of analytics. It comprises the pre-processing, cleaning, and preliminary examination of data. This course provides instruction in all aspects of exploratory data analysis. It reviews a wide variety of tools and techniques for preprocessing and cleaning data, including big data. It provides the student with practice in evaluating and plotting/graphing data to evaluate the content and integrity of a data set.

Prerequisites: None
Corequisites: None

ANLY 512 – Data Visualization (3 credits)

The visualization and communication of data is a core competency of analytics. This course takes advantage of the rapidly evolving tools and methods used to visualize and communicate data. Key design principles are used to reinforce skills in visual and graphical representation.

Prerequisites: ANLY 500, HCIN 500, or ISEM 542
Corequisites: None

ANLY 510 – Analytics II: Prin & Applications (3 credits)

This course takes an applied perspective and provides the statistical tools and analytic thinking techniques needed to: formulate a clear hypothesis, determine the most efficient method to obtain required data, determine and apply the proper statistical techniques to the resulting data, and effectively convey the results to both experts and laypersons. The course begins with a review of the descriptive analytics concepts (i.e., sampling, and statistical inferences) introduced in ANLY 500 as well as general conventions regarding experimentation and research. It then progresses to predictive and prescriptive analytics techniques such as regression and forecasting that can be used to predict future events. Later sessions focus on issues related to lack of experimental control (e.g., quasi-experimental design and analysis). The course culminates with a research project in which the student applies the concepts learned to their own research interests.

Prerequisites: ANLY 500, ANLY 502
Corequisites: None

ANLY 545 – Analytical Methods II (3 credits)

This course provides student with exposure to an expanded range of analytical methods. This includes additional functions, e.g. the logit function, additional distributions, e.g. Poisson distribution, and additional analysis techniques, e.g. those included in the study of discrete structures such as combinatorics. Particular attention is paid to analytics relevant to disciplines in the social sciences. Also included are survey design, development and (survey data) analysis.

Prerequisites: ANLY 502
Corequisites: None

ANLY 560 – Funct Programming Methds for Anly (3 credits)

This course provides the student with the required knowledge and skills to handle and analyze data using a variety of programming languages as well as a variety of programming tools and methods. Depending on current industry standards, the student will be provided with the opportunity to develop knowledge and skills in programming environments such as R, Octave, and Python. In addition, the student is introduced to current industry standard data analysis packages and tools such as those in Matlab, SAS or SPSS.

Prerequisites: None
Corequisites: None

GRAD 695 – Research Methodology & Writing (3 credits)

This course guides the student to develop and finalize a selected research problem and to construct a proposal that effectively establishes the basis for either writing a thesis or launching an experiential capstone project. The course provides an overview of strategies for effective problem investigation and solution proposal. Research methodology is studies and applied as part of suggesting a solution to a problem. Writing and formatting techniques are also explored and applied as a communication tool for cataloging the investigation and recommending the solution.

Prerequisites: Completion of at least 18 graduate semester hours; must be taken prior to GRAD 699
Corequisites: None

International Admissions

Information for International Students

All of the University’s graduate programs are STEM approved, and curricular practical training (CPT) is offered for this program.

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