Master of Science in Analytics (ANMS)

This 36-semester hour program prepares the student by providing depth in analytics during the first year and focused functional study during the second year that can be applied to any discipline or any interdisciplinary area.  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.

The Analytics student may specialize in the following areas:  Natural Language Processing, Machine Learning, Forecasting or an individualized concentration.

Program Goals

 ANMS graduates are able to:

  • Identify and assess the opportunities, needs and constraints for data usage;
  • Make clear and insightful analyses changing direction quickly as required by these analyses;
  • Identify new opportunities, find better explanations or discover and creatively solve problems using insights developed through data analyses;
  • Communicate clearly and persuasively to a variety of audiences;
  • Maintain ethics throughout the conduct and use of analyses and results; and,
  • Lead analytics teams and projects.

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

Analytics Requirements – The following courses comprise the Master of Science in Analytics program – 36 semester hours.  The semester hour value of each course appears in parentheses ( ).

Complete all of the following Core courses – 15 semester hours:

ANLY 500
Analytics I:  Principles and Applications
(3 credits)
ANLY 510 
Analytics II:  Principles and Applications
(3 credits)
ANLY 502
Analytical Methods I
(3 credits)
ANLY 506
Exploratory Data Analysis
(3 credits)

or

ANLY 512
Data Visualization
(3 credits)
ANLY 545
Analytical Methods II
(3 credits)

or

ANLY 560
Functional Programming Methods for Analytics
(3 credits)

Complete the following Experiential courses – 6 semester hours:

GRAD 695
Research Methodology and Writing
(3 credits)

and

ANLY 699
Applied Project in ANLY
(3 credits)

or

GRAD 699
Graduate Thesis
(3 credits)

Complete one of the following concentrations:

Natural Language Processing Concentration:

ANLY 506
Exploratory Analyses
(3 credits)

or

ANLY 512
Data Visualization
(3 credits)

(Select the course not taken as part of the core)

ANLY 520
Sentiment Analysis
(3 credits)
ANLY 540
Analysis of Human Language 
(3 credits)
ANLY 610
Analytical Methods III
(3 credits)
ANLY
Elective
(3 credits)

Machine Learning Concentration:

ANLY 506
Exploratory Analyses
(3 credits)

or

ANLY 512
Data Visualization
(3 credits)

(Select the course not taken as part of the core)

ANLY 530
Machine Learning I
(3 credits)
ANLY 535
Machine Learning II
(3 credits)
ANLY 610
Analytical Methods III 
(3 credits)
ANLY
Elective
(3 credits)

Forecasting Concentration:

ANLY 506
Exploratory Analyses
(3 credits)

or

ANLY 512
Data Visualization
(3 credits)

(Select the course not taken as part of the core)

ANLY 505
Modeling, Simulation and Gamification 
(3 credits)
ANLY 515
Risk Modeling and Assessment
(3 credits)
ANLY 525
Quantitative Decision-Making 
(3 credits)
ANLY 530
Machine Learning I
(3 credits)

Individualized Concentration:

The Master of Science in Analytics student can choose courses totaling 15 semester hours of credit from any of the following Master of Science programs: Analytics, Information Systems Engineering and Management, Computer Information Sciences, Learning Technologies, or Project Management.


ANLY 500 Analytics I: Principles and Applications (3 semester hours)

Prerequisites:  MATH 220 and 280

Corequisites:  MATH 510 or demonstrated competency in mathematics, statistics, and applied statistics at the discretion of the advisor.

Description:  This course covers the core concepts and applications of analytics in different domains.  The student is introduced to the main concepts and tools of analytics (e.g., data querying and reporting, data access and management, data cleaning, statistical programming, data mining introduction, relational databases, and statistical analysis of databases).  The student is also introduced to the emerging topics in data sciences such as Big Data, Smart (Semantic) Data, data modeling, and data visualization.  The student then applies the principles of analytics/data sciences to different domains such as health, education, public safety, public welfare, transportation, and other public and private sectors.  The student is then encouraged to apply the concepts to a domain of interest.


ANLY 502 Analytical Methods I (3 semester hours)

Prerequisites: None

Description: 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.


ANLY 505 Modeling, Simulation and Gamification (3 semester hours)

Prerequisites:  MATH 220 and 280

Description:  This course covers the basic principles of mathematical modeling, Monte Carlo simulations, and gamification in modern enterprises.  The course draws upon interdisciplinary source material, real-world case studies, and production game environments to identify effective analytical models, strategies, techniques, and metrics for the application of games to business.  It also identifies a number of significant pitfalls to the successful implementation of gamification techniques, notably legal and ethical issues, the difficulty of making things fun, and the problems with implementing radical change in established firms.  The course’s emphasis is on how Big Data can be used to support the analytical models, simulations and games.


ANLY 506 Exploratory Data Analysis (3 semester hours)

Prerequisites: None

Description: 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 pre-processing 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.


ANLY 510 Analytics II: Principles and Applications (3 semester hours)

Prerequisites:  ANLY 500

Description:  This course provides a comprehensive background for the student who wants to engage in advanced analytics projects in the public and private sectors.  The student is exposed to descriptive, predictive as well as prescriptive analytics techniques.  The course begins with a review of the descriptive analytics concepts (i.e., sampling and statistical inferences) ANLY 500 that are used to discover and understand correlations.  It then concentrates on predictive analytics techniques such as regression, forecasting, and simulations that can be used to predict future events based on past data.  The course concludes with the perceptive analytics techniques that attempt to find the “best” solutions by using linear and non-linear optimization techniques and statistical decision models.  The student is strongly encouraged to apply the concepts to domains of interest.


ANLY 512 Data Visualization (3 semester hours)

Prerequisites: ANLY 500

Description: 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.


ANLY 515 Risk Modeling and Assessment (3 semester hours)

Prerequisites:  ANLY 500

Description:  This course focuses on risk management models and tools and the measurement of risk using statistical and stochastic methods, hedging, and diversification.  Examples of this are insurance risk, financial risk, and operational risk.  Topics covered include estimating rare events, extreme value analysis, time series estimation of external events, axioms of risk measures, hedging using financial options, credit risk modeling, and various insurance risk models.


ANLY 520 Sentiment Analytics (3 semester hours)

Prerequisites:  ANLY 500

Description:  Web technologies based on text and Natural Language Processing (NLP) are becoming the bone structure of the cloud.  Phones and handheld computers support predictive text and handwriting recognition; web search engines give access to information locked up in unstructured text; machine translation allows us to retrieve texts written in Chinese and read them in Spanish.  By providing more natural human-machine interfaces, and more sophisticated access to stored information, text language processing has come to play a central role in the multi-lingual information society.  This course provides a highly accessible introduction to the field of text mining and computational linguistics.  The course is intensely practical; it uses R and/or Python programming language together with fully worked examples and graded exercises.


ANLY 525 Quantitative Decision-Making (3 semester hours)

Prerequisites:  ANLY 515

Description: Decision-making in business today requires the use of all resources, particularly information.  Analytics supports decision-making quantitatively by applying information received from multiple sources.  This course provides the foundation for quantitative decision-making using a rational, coherent approach and includes decision-making principles and how they are applied to business challenges today.


ANLY 530 Machine Learning I (3 semester hours)

Prerequisites:  ANLY 510

Description: This course introduces the student to machine learning.  It provides the student with the cognitive, mathematical and analytical foundation required for machine learning.  It also provides the student with a broad overview of machine learning, including topics from data mining, pattern recognition and supervised and unsupervised learning.  This course prepares the student for the complex, higher-level topics in Machine Learning II.


ANLY 535 Machine Learning II (3 semester hours)

Prerequisites: ANLY 530

Description: Machine Learning II considers complex, high-level topics in machine learning.  It builds on the foundation provided by Machine Learning I to develop algorithms for supervised and unsupervised machine learning, to study and develop artificial neural networks, to study, develop and evaluate systems for pattern recognition and to consider trade-offs in models, for example, balancing complexity (e.g. volume, variety and velocity of big data) and performance.


ANLY 540 Analysis of Human Language (3 semester hours)

Prerequisites: ANLY 520

Description: Over 80% of the content held on big data systems is in the form of unstructured data.  The vast majority of the unstructured data is human language.  Presently, the prevailing techniques employed to analyze this data are at the levels of word and short phrase analysis, such as those found in the Sentiment Analytics course.  This course will move beyond these levels and introduce the student to advanced techniques used in computational linguistics and natural language processing.


ANLY 545 Analytical Methods II (3 semester hours)

Prerequisites: ANLY 502

Description: 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.


ANLY 560 Functional Programming Methods for Analytics (3 semester hours)

Prerequisites: None

Description:  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.


ANLY 580 Special Topics (3 semester hours)

Prerequisites: None

Description:  This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of analytics.


ANLY 585 Research in Analytics (3 semester hours)

Prerequisites: None

Description: This program cultivates and supports research partnerships between the student, faculty and other researchers.  It provides the student with the opportunity to work on cutting-edge research.  Research projects can be at any appropriate and approved level; introductory, participatory or expert.  Each project requires an approved proposal, periodic status reports and a final written report with a presentation prepared by the student in collaboration with the research supervisor.


ANLY 600 Optimized Analytics (3 semester hours)

Prerequisites:  ANLY 510

Description:  This course introduces the fundamental tool in prescriptive analytics.  Optimization is the process of selecting values of decision variables that minimize or maximize some quantity of interest.  Optimization models have been used extensively in operations and supply chains, finance, marketing, and other disciplines to help managers allocate resources more effectively and make lower cost or more profitable decisions.


ANLY 610 Analytical Methods III (3 semester hours)

Prerequisites: None

Description: This course provides the student with exposure to the theoretical background for advanced analytical topics and methods.  Topics include unstructured data/information and big data.  For example, the theoretical background required for the integration of data mining and text analytics or text mining are explored.  Additional topics could include the implementation and use of data lakes and ontology evaluation.


ANLY 699 Applied Project in Analytics (3 semester hours)

Prerequisites:  GRAD 695 and permission of instructor

Description:  This course allows the student to pursue an area of interest that is within the broad scope of analytics.  A faculty member will supervise this study.