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Courses & Requirements

Course Descriptions

All classes are 3 credit hours unless otherwise noted. 

  • Click here to read complete descriptions of the Master of Analytics courses offered at Wittenberg.

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 520. Business Intelligence and Analytics Fundamentals.
This course provides an introduction to Business Intelligence, including the processes, methodologies, 350 infrastructure, and current practices used to transform data into useful information and support decision-making. Business Intelligence requires foundation knowledge in data storage and retrieval, thus this course will review logical data models for both database management systems and data warehouses. Students will learn to extract and manipulate data from these systems and assess security- related issues. Data mining, visualization, and statistical analysis along with reporting options such as management dashboards and balanced scorecards will be covered.

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 provides students with a rigorous course of study in all three areas of analytics: Descriptive, predictive, and prescriptive techniques. The techniques in each respective area will lead students in a journey that begins first with understanding what the data are describing in a given situation. Students then focus on evaluating data to predict likely outcomes in varied situations. The course also exposes students to cutting edge techniques for using data to inform best practice in a wide range of contexts. Coverage of each topic is complemented by case studies, team projects, and guest speakers from industry, ensuring an academic experience that is well grounded.

ANLT 550. Data Visualization. 
This course will introduce students to the field of data visualization. Students will learn data visualization design and evaluation principles, and learn how to acquire, parse, and analyze large datasets. Students will also learn techniques for visualizing multivariate, temporal, textbased, geospatial, hierarchical, and network/graph-based data. 

ANLT 560. Data Management.
The course examines issues related to data organization, representation, access, storage, and processing. This will include topics such as metadata, data storage systems, self-descriptive data representations, semi-structured data models, ontology, semantic web, and large-scale data analysis. Developing and managing data requires understanding the fundamentals of database systems, techniques for designing databases, and principles of database applications and administration. This course provides an introduction to the fundamental concepts and practices of relational database systems. In addition this course will introduce the student to the major activities involved in data warehousing. The class will begin with an in-depth review of baseline data warehouse principles and concepts. Once the basic principles have been established, the remainder of the class will be built around a group data warehouse project.

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 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.
The capstone project is designed to demonstrate your accumulated training in analytics in a single original project of your choice, subject to the instructor’s approval and under the additional supervision of a faculty mentor. The capstone project will be completed in four phases, each with its own specific focus: project exploration, project design and proposal, project execution and project finalization and reporting. The completed thesis or project should bring together your theme, focus, expertise, and practitioner experience. The Capstone necessitates multiple drafts of your research that are subjected to heightened review and regular feedback from your instructor, your peers and your mentor. By the completion of the project, students will be able to clearly articulate the nature, relevance and context of the problem, related research questions, methods, and results in a well-written and orally presented project report.

ANLT 592. Analytics Capstone II (Project Design and Proposal). 2 credits.
The capstone project is designed to demonstrate your accumulated training in analytics in a single original project of your choice, subject to the instructor’s approval and under the additional supervision of a faculty mentor. The capstone project will be completed in four phases, each with its own specific focus: project exploration, project design and proposal, project execution and project finalization and reporting. The completed thesis or project should bring together your theme, focus, expertise, and practitioner experience. The Capstone necessitates multiple drafts of your research that are subjected to heightened review and regular feedback from your instructor, your peers and your mentor. By the completion of the project, students will be able to clearly articulate the nature, relevance and context of the problem, related research questions, methods, and results in a well written and orally presented project report.

ANLT 593. Analytics Capstone III (Project Execution). 2 credits.
The capstone project is designed to demonstrate your accumulated training in analytics in a single original project of your choice, subject to the instructor’s approval and under the additional supervision of a faculty mentor. The capstone project will be completed in four phases, each with its own specific focus: project exploration, project design and proposal, project execution and project finalization and reporting. The completed thesis or project should bring together your theme, focus, expertise, and practitioner experience. The Capstone necessitates multiple drafts of your research that are subjected to heightened review and regular feedback from your instructor, your peers and your mentor. By the completion of the project, students will be able to clearly articulate the nature, relevance and context of the problem, related research questions, methods, and results in a well-written and orally presented project report.

ANLT 594. Analytics Capstone IV (Project Finalization and Reporting). 1 credit. The capstone project is designed to demonstrate your accumulated training in analytics in a single original project of your choice, subject to the instructor’s approval and under the additional supervision of a faculty mentor. The capstone project will be completed in four phases, each with its own specific focus: project exploration, project design and proposal, project execution and project finalization and reporting. The completed thesis or project should bring together your theme, focus, expertise, and practitioner experience. The Capstone necessitates multiple drafts of your research that are subjected to heightened review and regular feedback from your instructor, your peers and your mentor. By the completion of the project, students will be able to clearly articulate the nature, relevance and context of the 353 problem, related research questions, methods, and results in a well-written and orally presented project report.

Analytics Capstone

As in construction; a capstone is the final piece on top of the wall perfectly summarizing all the hard work that has gone into building the perfect fixture.

A totally unique feature of our program is the requirement that each student design and complete an original project that is professionally relevant or personally passionate to them. Students will not be forced to complete the same assignment rather they will be able to choose something that is both applicable and important to them. Each student will benefit from faculty and advisory board professionals who will coach each project to the finish. Each student will present to a panel of professionals.

The Capstone courses are held on Saturdays on campus and often include workshops, software demos, and speakers. The capstone is designed to demonstrate your accumulated learning of analytics.

 

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