Master of Science
Core courses without a concentration (21 hrs)
Provides an overview of quantitative methods essential for analyzing data, with an emphasis on business and industry applications. Standard and open source statistical packages are used to apply techniques to real-world problems.

ADTA 5130A
Exploratory Data Analysis & Probability

ADTA 5130B
Sampling, Inference, and Hypothesis Testing

ADTA 5130C
Correlation & Regression: Generating Solutions
Extends the concepts developed in Data Analytics I to multivariate and unstructured data analysis. Modern techniques of multivariate analysis, including association rules, classification methods, time series and text analysis are explored and implemented with real-world business and industry data. Provides a hands-on introduction to state-of-practice technology and tools. Focus is on the application and interpretation of the methods discussed.
Prerequisite(s): ADTA 5130 or consent of instructor.

ADTA 5230A
Coming Soon

ADTA 5230B
Coming Soon

ADTA 5230C
Coming Soon
Provides an introduction to collecting, storing, managing, retrieving and processing datasets. Techniques for large and small datasets are considered, as both are needed in data science applications. Emphasizes applications and includes many hands-on projects.

ADTA 5240A
Storing and Harvesting Data

ADTA 5240B
Principles of Data Wrangling and Queries

ADTA 5240C
Principles of Data Structures
Presents strategies and methods for effective visualization and communication of large data sets. Standard and open source data visualization packages are used to develop presentations that convey findings, answer business questions, drive decisions and provide persuasive evidence supported by data.
Prerequisite(s): None.
This course covers the latest methods for discovery and learning from large data sets. Topics complemented by hands-on projects using data discovery and statistical learning software.

ADTA 5340A
Coming Soon

ADTA 5340B
Coming Soon

ADTA 5340C
Coming Soon
Application of advanced analytics to case study projects designed to provide experience in solving complex industry and business problems, determining solutions that address project objectives, selecting appropriate methods among various possible alternatives, applying techniques and technology in real-world settings, and attaining proficiency in the deployment of analytics, including professional communication.
Prerequisite(s): ADTA 5130, ADTA 5230, ADTA 5250, ADTA 5240, ADTA 5340.
Open to all student seeking an analytics capstone course. This unique learn-by-doing course is offered in lieu of a project, portfolio or thesis for candidates of the MS Advanced Data Analytics degree. Requires a significant project about which students periodically report, highlighting the interdisciplinary nature of their findings and its relevance to their interests and/or career goals. Students and peers discuss how their ongoing effort enriches and advances the human condition. Submission of a final paper and presentations are required for successful completion.
Prerequisite(s): Completion of required 18 hours of Advanced Data Analytics core courses toward degree; consent of instructor.
Electives (9 hrs)
Three elective courses (9 hours) are required after consultation with Jessica Temple, Advanced Data Analytics Academic Counselor.
Core courses with a concentration (18 hrs)
Provides an overview of quantitative methods essential for analyzing data, with an emphasis on business and industry applications. Standard and open source statistical packages are used to apply techniques to real-world problems.
Extends the concepts developed in Data Analytics I to multivariate and unstructured data analysis. Modern techniques of multivariate analysis, including association rules, classification methods, time series and text analysis are explored and implemented with real-world business and industry data. Provides a hands-on introduction to state-of-practice technology and tools. Focus is on the application and interpretation of the methods discussed.
Prerequisite(s): ADTA 5130 or consent of instructor.
Provides an introduction to collecting, storing, managing, retrieving and processing datasets. Techniques for large and small datasets are considered, as both are needed in data science applications. Emphasizes applications and includes many hands-on projects.
This course covers the latest methods for discovery and learning from large data sets. Topics complemented by hands-on projects using data discovery and statistical learning software.
Application of advanced analytics to case study projects designed to provide experience in solving complex industry and business problems, determining solutions that address project objectives, selecting appropriate methods among various possible alternatives, applying techniques and technology in real-world settings, and attaining proficiency in the deployment of analytics, including professional communication.
Prerequisite(s): ADTA 5130, ADTA 5230, ADTA 5250, ADTA 5240, ADTA 5340.
Open to all student seeking an analytics capstone course. This unique learn-by-doing course is offered in lieu of a project, portfolio or thesis for candidates of the MS Advanced Data Analytics degree. Requires a significant project about which students periodically report, highlighting the interdisciplinary nature of their findings and its relevance to their interests and/or career goals. Students and peers discuss how their ongoing effort enriches and advances the human condition. Submission of a final paper and presentations are required for successful completion.
Prerequisite(s): Completion of required 18 hours of Advanced Data Analytics core courses toward degree; consent of instructor.
Concentrations
Strategic perspective of fashion-oriented products in a dynamic marketplace. Included are case analyses of merchandising principles practiced by representative companies. Interpretations of global trends and issues affecting multi-channel distribution.
Study of web site interface design principles, web usability and digital merchandising tools for optimizing digital retailing performance. Analysis and applications of consumer data to design and manage consumer experience in digital platforms.
Prerequisite(s): Basic knowledge and understanding of statistical terminologies and proficiency in Excel are required for customer data analysis.
Analysis and application of digital information exchange technology related to textile, apparel, home furnishings and other fashion-oriented products. Emphasis on distribution, merchandising, e-commerce and sales.
Prerequisite(s): None.
Classic and contemporary consumer theories analyzed in situational contexts. Emphasis on formulating integrated consumer behavior models for strategic decision-making in both domestic and international consumer-driven markets in merchandising and hospitality industries.
Prerequisite(s): None.
Creating and managing customer experiences of tangible and intangible products and services that link merchandising and hospitality segments. Applying merchandising strategies of planning, developing and presenting products to consumers with the experiential components of the hospitality industry to provide a total concept-based experience.
Prerequisite(s): None.
Overview of entire subject of computer and data applications in clinical and integrated services. Examination of management and electronic information systems across the continuum of long-term care and larger systems of care, plus their interface with complex regulatory and reimbursement systems. Primary issues include data security, storage and retrieval, management analysis, reporting, and transmission and interfacing.
Prerequisite(s): None.
With the help of case studies, reviews the evolution of management in the healthcare industry, and provides management theory, principles, methods and tools for managers in a variety of healthcare delivery settings. Explores key roles in healthcare organizations, as well as project planning and execution, managing change, personnel management and ethics in the healthcare environment.
Prerequisite(s): None.
Presents a broad overview of healthcare finance and focuses on tasks that are essential to the operational management of healthcare services, including estimating costs and profits, planning and budgeting, analyzing new equipment purchases, using metrics to monitor operations, and working with financial statements. Designed for individuals seeking basic skills in healthcare financial management.
Prerequisite(s): None.
Reviews the legal, regulatory and economic forces that shape the marketing of health services in today’s environment. With the integration of real work organizational examples, students explore the evolution of healthcare marketing from strategies based on advertising and promotion to current strategies that incorporate research, education, and the responsibility to understand the market in which healthcare organizations operate, the customers served by such organizations, and the customer’s needs, wants, behaviors and motivations.
Prerequisite(s): None.
Research emphasis in organizational behavior stressing organization-people linkages and interrelationships, including selection, orientation and training; job design and reward systems; supervision; formal participation schemes; appraisals and development; organizational structure and design; communications; control; and conflict resolution. Examination of behavioral science methodologies and strategies. Applications to tangential areas of organization theory, development, planning and implications for management and employee relations.
Prerequisite(s): None.
Examination and evaluation of current theories, issues and programs involved in strategically managing organizations. Emphasis is on critical thinking, judgment and solving strategy problems within uncertain and complex decision environments.
Prerequisite(s): None
Theories and current research on leadership with emphasis placed on leadership development and specific applications within the organizational setting.
Prerequisite(s): None.
Examination of the development of organizational competencies and capabilities through the study of the theory and tools related to organizational design and change. Emphasis is placed on the use of horizontal and vertical linkage mechanisms that provide the organization with the flexibility to adapt to a rapidly changing competitive environment. Definition of management roles and the use of teams are emphasized in the change management process.
Prerequisite(s): None.
Creation of new business enterprises and the expansion of current enterprises through the venture. Topics include assessment of entrepreneurial characteristics, the entrepreneurial team, generation and screening of venture ideas, market analysis and technical analysis.
Prerequisite(s): None.
Background
In order to be successful in the program, a background class in applied statistics, business statistics, ADTA 5100 Fundamentals of Data Analytics or an equivalent course is required.
For additional information, contact analytics@unt.edu.
Visit the Registrar's website for more information on course registration dates and payment deadlines.
Introduces fundamental concepts of contemporary statistics with an emphasis on applications and computational methods. Topics include classical inference and related numerical optimization methods; Bayesian inference and Monte Carlo methods for density estimation; jackknife, bootstrap, and related nonparametric methods for assessing statistical accuracy, obtaining linear regression solutions, and performing hypothesis tests; estimation of functions. Focuses on applications of statistical methods to addressing important problems in business, science, and industry.
Prerequisite(s): Undergraduate probability or statistics course, or ADTA 5130, or consent of instructor.
Examination and evaluation of current theories, issues and programs involved in strategically managing organizations. Emphasis is on critical thinking, judgment and solving strategy problems within uncertain and complex decision environments.
Prerequisite(s): ADTA 5610, equivalent probability course, or consent of instructor.
Introduction to statistical methods, experimental design, data presentation and hypothesis testing in biological research. Statistical inference includes tests for normality, skewness, kurtosis, and two-sample data sets for goodness of fit, contingency, means, medians and non-parametric methods. Introduces probability and SAS software.
Prerequisite(s): MATH 1100.
Continuation of Biostatistics I. Statistical methods and experimental designs in biological research. Coverage of parametric and non-parametric correlation, multi-sample inference tests (ANOVA) including one-way, block, nested and factorial designs; multiple range (comparison) analyses; simple linear, non-linear and multiple regressions; ANCOVA. Introduces multiple variable approaches including discriminate, factor and cluster analysis.
Prerequisite(s): MATH 1100, BIOL 5130.
Introduction to computational problems inspired by the life sciences and overview of available tools. Methods to compute sequence alignments, regulatory motifs, phylogenetic trees and restriction maps.
Prerequisite(s): None.
Application of computational methods to problems in the fields of public health. Design and implementation of disease outbreak models.
Prerequisite(s): None.
Topics of current interest that vary from year to year.
Prerequisite(s): Consent of department.
Develop and implement the computational and data infrastructure needed to support data analytics. Understand exploratory data analysis (EDA) and exploratory spatial data analysis (ESDA) methods and appropriate ways of applying them to a variety of unstructured datasets. Use geovisualization techniques to communicate and interpret information learned from data.
Prerequisite(s): Consent of department.
Develops the tools necessary to analyze, interpret, and develop empirical applications of econometric estimation procedures. Students explore an assortment of applied problems that are typically encountered in quantitative research with particular attention given to the examination of real world, economic and business-related phenomena. Particular attention is given to developing proficiency in the following areas: organizing and manipulating data, estimating linear regression models, interpreting econometric results and computer output, and working with computer software.
Prerequisite(s): ECON 5640.
Focuses on time series analysis and forecasting methodologies applied to problems typically encountered in economics, finance, and accounting. Topics include AR, MA and ARMA models; dynamic time series models; non-stationarity and tests for unit roots; ARCH and GARCH models; VAR models and impulse response functions; fractional integration and cointegration; and error correction models. Computer applications will be used to reinforce the theoretical models.
Prerequisite(s): ECON 5640 or consent of department.