It is not enough just to collect data. Businesses have to derive actionable insights
from the data to make timely, informed decisions. Doing this requires people with
advanced data analytics training.
UNT's 30-hour Master of Science in Advanced Data Analytics provides the breadth and
depth of experiences to enable you to succeed in a data-driven business world. Our
online courses are offered in an accelerated 8-week format. The in-person classes
at our Denton and Frisco campuses are the traditional 16-week format that blends in-person
and online instruction. You can choose an existing specialization or work with the
advisor to develop one that fits your needs. We offer optional concentrations in Analytics
Project Management, Applied Artificial Intelligence, Digital Retailing, Geographic
Information Systems, Geospatial Intelligence, Health Data Analytics, Management and
Statistics.
Combining analytics foundations, machine learning, cloud computing, and data visualization
with real business case studies, this degree will help you apply the latest analytic
techniques in your current job or help launch your career in an expanding job market.
We also offer in-person course options at UNT at Frisco and our main campus in Denton for
those in the Dallas-Fort Worth area.
Advance in your career and take the next step toward your data analytics career goals.
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.
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.
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 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
ADTA 5810 Managing Analytics Projects
Introduces project management principles and concepts, providing a foundation for
managing data analytics projects effectively. Addresses project management knowledge
areas, roles and responsibilities of analytics teams, and agile practices to facilitate
the production of industry-standard artifacts.
Prerequisite(s): None.
ADTA 5820 Analytics Leadership and Communication
Develops an understanding of the theory and practice of leadership in organizational
settings commonly encountered by analytics professionals. Develops and practices persuasive
communication methods essential for effective leadership of analytics teams.
Prerequisite(s): None.
ADTA 5830 Risk Management and Value Creation for Analytics
Examines policies, practices, regulations, and governance for analytics projects to
reduce risk and create value. Provides an understanding of how to identify and manage
risk and maintain value through quality and procurement management and stakeholder
engagement.
Prerequisite(s): None.
ADTA 5840 Agile Frameworks for Analytics
Examines Agile frameworks and practices for analytics teams and projects. Facilitates
the development of an Agile mindset and focuses on how to create business value through
the values and principles of Agile.
Prerequisite(s): None.
ADTA 5550 Deep Learning with Big Data
Introduction to fundamentals of artificial neural networks with big data applications.
Theory and implementation of deep learning techniques to obtain solutions to business,
industry, and science problems. Applications of deep learning frameworks and libraries.
Prerequisite(s): ADTA 5240 or ADTA 5250 or ADTA 5340.
ADTA 5560 Recurrent Neural Networks
Fundamentals and practical implementations of Recurrent Neural Networks, focusing
on Long Short-Term Memory (LSTM) networks. Emphasis on applying current AI frameworks
to build artificial neural networks for deep learning solutions to problems in business,
industry, and science. The course provides the student with a guide through how to
use TensorFlow and Keras, the two most popular AI frameworks at present, to build
artificial neural networks for deep learning that will be trained on the sequence
data of which time series is one example. Covers both the theory and the practical
implementation of the AI network. As the fundamentals are discussed, exemplary AI
techniques will be employed to illustrate how AI deep learning theories can be applied
to real-world solutions using various programming and system tools.
Prerequisite(s): One of the courses: ADTA 5240, ADTA 5250, ADTA 5340, or ADTA 5550,
or consent of instructor.
ADTA 5750 Applied Natural Language Processing
Introduces fundamentals of Natural Language Processing (NLP), providing a guide to
applying novel and pre-trained NLP systems in business and other real-world environments.
Presents contemporary methods and tools used to perform a variety of language-related
analysis, such as text understanding and text classification, in a low-code development
environment. Emphasizes the practical implementation of Natural Language Processing
methods to solving business, industry and science problems.
Prerequisite(s): ADTA 5340, ADTA 5550, ADTA 5560, or consent of instructor.
ADTA 5760 Natural Language Processing with AI
Introduces theory and the practical implementation of Natural Language Processing
(NLP) using artificial neural networks. Provides experience applying current neural
network frameworks to build, train, and test NLP models. Emphasizes the practical
implementation of AI techniques to develop NLP solutions for business, industry, and
science applications.
Prerequisite(s): ADTA 5340, ADTA 5550, ADTA 5560, or consent of instructor.
MDSE 5240 - Global Retailing
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.
MDSE 5710 - Digital Optimization
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.
MDSE 5750 - Digital Retailing
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.
CMHT 5440 - Consumer Theory
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.
CMHT 5600 - Managing Customer Experiences
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.
GEOG 5510 - GIS for Applied Research
Introduces basic geography and Geographic Information System (GIS) concepts and techniques
to enable comprehensive analyses of geospatial data. Integrates data from multiple
sources to address research in a variety of disciplines. Facilitates geospatial analyses
and mapping for integration into other university courses and research projects.
Prerequisite(s): None.
GEOG 5525 - LiDAR Data Analysis in GIS
Overview of LiDAR principles and data processing methods. Focus on LiDAR data analytical
skills in a GIS environment through exercises and individual research project on topics
related to forestry/vegetation mapping and measurement, urban environments, and geosciences.
Prerequisite(s): GEOG 3500, GEOG 5510 or equivalent.
GEOG 5530 - Remote Sensing and Digital Image Analysis
In-depth analysis of image processing including image composition, enhancement and
interpretation, and the principles and practices of photo interpretation and remote
sensing for use in a variety of disciplines, as in environmental and ecological science.
Students conduct independent research project on an application area of digital image
analysis.
Prerequisite(s): GEOG 5510 or equivalent.
GEOG 5540 - Enabling Business Intelligence Using Enterprise GIS
Focus on the computational infrastructure needed to gather geospatial and business
intelligence. Develop solutions to clarify, streamline, and/or improve processes for
the collection, management, and utilization of geospatial data.
Prerequisite(s): None.
GEOG 5560 - Application Development with Python Programming
Developing customized computer applications for efficiently processing and managing
data is vital to fulfill needs that are not met by existing, off-the-shelf software.
Examines Python programming concepts, input and output, logic structures, data structures,
and object-oriented programming. Python applications are developed through a series
of mini-projects covering a variety of tasks including data extraction from online
sources, data manipulation and management in relational database management systems,
and graphing and visualization.
Prerequisite(s): None.
GEOG 5590 - Advanced GIS Programming
Methods of creating new applications and improving productivity in GIS through computer
programming. Culminates in an advanced-level programming project. Topics include accessing
maps and data layers, querying and selecting features, updating databases, and accessing
raster and TIN/Terrain layers.
Prerequisite(s): GEOG 5560 or consent of department.
GEOG 5510 - GIS for Applied Research
Introduces basic geography and Geographic Information System (GIS) concepts and techniques
to enable comprehensive analyses of geospatial data. Integrates data from multiple
sources to address research in a variety of disciplines. Facilitates geospatial analyses
and mapping for integration into other university courses and research projects.
Prerequisite(s): None.
GEOG 5525 - LiDAR Data Analysis in GIS
Overview of LiDAR principles and data processing methods. Focus on LiDAR data analytical
skills in a GIS environment through exercises and individual research project on topics
related to forestry/vegetation mapping and measurement, urban environments, and geosciences.
Prerequisite(s): GEOG 3500, GEOG 5510 or equivalent.
GEOG 5530 - Remote Sensing and Digital Image Analysis
In-depth analysis of image processing including image composition, enhancement and
interpretation, and the principles and practices of photo interpretation and remote
sensing for use in a variety of disciplines, as in environmental and ecological science.
Students conduct independent research project on an application area of digital image
analysis.
Prerequisite(s): GEOG 5510 or equivalent.
GEOG 5540 - Enabling Business Intelligence Using Enterprise GIS
Focus on the computational infrastructure needed to gather geospatial and business
intelligence. Develop solutions to clarify, streamline, and/or improve processes for
the collection, management, and utilization of geospatial data.
Prerequisite(s): None.
GEOG 5560 - Application Development with Python Programming
Developing customized computer applications for efficiently processing and managing
data is vital to fulfill needs that are not met by existing, off-the-shelf software.
Examines Python programming concepts, input and output, logic structures, data structures,
and object-oriented programming. Python applications are developed through a series
of mini-projects covering a variety of tasks including data extraction from online
sources, data manipulation and management in relational database management systems,
and graphing and visualization.
Prerequisite(s): None.
GEOG 5590 - Advanced GIS Programming
Methods of creating new applications and improving productivity in GIS through computer
programming. Culminates in an advanced-level programming project. Topics include accessing
maps and data layers, querying and selecting features, updating databases, and accessing
raster and TIN/Terrain layers.
Prerequisite(s): GEOG 5560 or consent of department.
HLSV 5300 - Information Systems for Healthcare Management
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.
HLSV 5450 - Health Services Administration
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.
HLSV 5740 - Financial Issues in Health Services Administration
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.
HLSV 5820 - Marketing Health Services
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.
MGMT 5140 - Organizational Behavior and Analysis
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.
MGMT 5760 - Strategic Management
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
MGMT 5870 - Leadership Research and Development
Theories and current research on leadership with emphasis placed on leadership development
and specific applications within the organizational setting.
Prerequisite(s): None.
MGMT 5120 - Managing Organizational Design and Change
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.
MGMT 5300 - Entrepreneurship and Venture Management
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.
ADTA 5610 - Applied Probability Modeling for Data Analytics
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.
ADTA 5620 - Applied and Computational Statistics for Data Analytics
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.
BIOL 5130 - Biostatistics I
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.
BIOL 5140 - Biostatistics II
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.
BIOL 5810 - Biocomputing
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.
BIOL 5820 - Computational Epidemiology
Application of computational methods to problems in the fields of public health. Design
and implementation of disease outbreak models.
Prerequisite(s): None.
MATH 5700 - Selected Topics in Contemporary Mathematics
Topics of current interest that vary from year to year.
Prerequisite(s): Consent of department.
GEOG 5195 - Advanced Geospatial Data Analytics
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.
ECON 5645 - Empirical Linear Modeling
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.
ECON 5660 - Time Series Econometrics and Forecasting
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.
University of North Texas 
