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Courses & Requirements

Course Descriptions

All classes are 3 credit hours unless otherwise noted. 
 

ANLT 500. Design Challenges of Wicked Data Problems.
A wicked data problem is a problem that is difficult or impossible to solve because of incomplete, contradictory, and changing requirements that are often difficult to recognize. The use of term "wicked" has come to denote problems that are resistant to resolution. Moreover, because of complex interdependencies, the effort to solve one aspect of a wicked problem may reveal or create other problems. This course will introduce students to the program by exploring a diverse set of complex problems – and relevant data – across many facets of life and work. In doing so, students will be introduced to the terms, concepts and techniques of data analysis as a basis for future courses and projects.

ANLT 510. Advanced Statistics and Modeling.
 This course develops fundamental knowledge and skills for applying statistics to decision making. Topics include descriptive statistics, probability distributions, sampling, confidence intervals, hypothesis testing and the use of computer software for statistical applications.

ANLT 530. Data Mining.
Data mining, or intelligent analysis of information stored in data sets, has recently gained a substantial interest among practitioners in a variety of fields and industries. Almost every organization now collects data, which can be analyzed in order to make better decisions, improve policies, discover computer network intrusion patterns, design new drugs, detect credit fraud, make accurate diagnoses, predict important events, monitor, evaluate reliability and preempt failures of complex systems, etc. This course will provide the participants with understanding of the fundamental data mining methodologies, and with the ability to formulate and solve problems with them. Particular attention will be paid to practical, efficient and statistically sound techniques. Lectures will be complemented with hands-on experience with data mining software. Students will have a chance to develop intuition needed to effectively evaluate and analyze data.

ANLT 540. Descriptive, Predictive and Prescriptive Analytics. 
This course covers descriptive, predictive, and prescriptive areas of analytics.  Students use cutting edge techniques from each area to understand data in specific context, and focus on evaluating data to predict likely outcomes. Case studies, team projects, and guest speakers from industry will be employed. 

ANLT 550. Data Visualization. 
This introductory course in data visualization focuses on design and evaluation principles, along with techniques, concepts, and idioms frequently used to engage and inform others. Focus is on how data visualizations both present data and expose insights. Students acquire, parse, and analyze large datasets, and use techniques for visualizing multivariate, temporal, textual, geospatial, hierarchical, and network data. 

ANLT 565. Marketing Analytics.
This course explores the intersection of data science and marketing principles to more effectively support business decisions. Students will assess marketing challenges, evaluate marketing data, and develop plans of action. Emphasis will be placed on providing corporate decision makers with relevant marketing analytic insights through the assigned projects. Prerequisite: none

ANLT 570. Case Studies I: The Power and breadth of analytics.
This course will provide an introduction to analytical methods for a variety of industries and contexts. Key management issues in each situation will be evaluated, and concepts learned throughout the program will be applied to demonstrate the potential of data and analytics to add value. The goal of this course will be to convey the breadth of the field and explore contextual differences in the application of analytics techniques.

ANLT 575. Data Driven Decisions.
This course covers the fundamental basis of using data to make decisions in time-bound, real world situations. Heuristics must be merged with proactive operational data systems to create quick, effective, and efficient decision-making. Real world cases, along with heuristic data analytics systems and approaches will be employed to challenge students to efficiently leverage data to make decisions. Prerequisites: none

ANLT 580. Case Studies II : Targeted Applications of Analytics. 
This class will be used to explore applications of analytics and open problems of particular interest to the class. In some instances, case studies will complement the capstone experience. Whereas the capstone projects will involve substantial depth and time to complete, this course will require students to assess problems quickly, evaluate data efficiently and develop plans of action that can add value in real time. This experience is designed to mimic the conditions all students will face when they put their skills to work following completion of the program. The overarching goal of this course is to demonstrate to cohort members the breadth of applications – and depth within these applications – where their analytics skills are relevant. This course is also designed to expand the range of skills and perspective of all members of the class.

ANLT 591. Analytics Capstone I (Project Exploration). 1 credit.
Phase one of Analytics Program capstone.  Students will review and practice graduate level research, University library resourses, literature reviews, data gathering and writing expectations.  The fianl project will challenge students to articulate the nature, relevance and context of a data problem, related research questions, methods, and produce results in a written report.

ANLT 592. Analytics Capstone II (Project Design and Proposal). 2 credits.
Phase two of Analytics Program capstone.  Course focuses on unrealized opportunities for using data for strategic decision making, and the intense problem solving faced by corporate managers and analysts.  Data sets from human resources, finance, marketing, operations, shipping and logistics, and production are analyzed.  Students articulate the nature, relevance and context of a data problem, related research questions, methods, and results in a video presentation. 

ANLT 593. Analytics Capstone III (Project Execution). 2 credits.
Phase three of Analytics Program capstone.  Students will execute a data analysis project for a company. Students work with a corporate partner to analyze a current business problem and review internal data needed to find a solution. Students will interact virtually with corporate stakeholders. 

ANLT 594. Analytics Capstone IV (Project Finalization and Reporting). 1 credit.
Phase four of Analytics Program capstone.  Course focus is production of a major research project utilizing knowledge, skills and techniques from previous coursework.  Students select topic, gather data, use library resources for literature review, use R as a base of data analysis, and write solution paper for publication in data, analysis, or business journal. Projects presented to panel of faculty and industry experts.

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