Online Master's Degree MS in Data Analytics

We live in a world where substantial amounts of data are available at the touch of a button. While this may be a very empowering prospect, it can also be overwhelming. In this course, students will examine the status of Big Data and its impact on the business world, framing analytics challenges using a structured life cycle approach to data analytics problems. Emphasis will be placed upon the verification of data, analytic techniques and open source tools for analyzing data, the role of regulatory organizations, and the privacy and ethics issues that surround its use.

Understanding the complexity of current data management systems and the ever evolving technology necessary to leverage such data is essential in making sound data-driven decisions. In this course, students will examine the issues in managing data and information from an enterprise perspective, and explore data management as an essential resource to organizational success through a deeper understanding of the concepts and techniques for managing the design, development, and maintenance of all the components of enterprise information management. The course will examine the roles and responsibilities of the various professionals that manage data and information in an organization.

The role of many analysts is as much about interpreting the results of data analysis as it is about gathering the data and "crunching the numbers." In this course, students will learn how to evaluate data in context, interpret data trends, and receive an overview of decision support management techniques such as predictive modeling, risk assessment and optimization, and analytics algorithms, which will set the stage for more advanced study in subsequent courses. Concepts from enterprise data management, including data warehousing and business intelligence, will provide a foundation for examining the topics of data mining, advanced and dimensional data modeling, and decision support system development as techniques for an organization's competitive advantage.

In addition to the gathering and interpretation of data, today's business environment calls upon the analyst to communicate the results of data analysis to a variety of audiences. In this course students will learn how to synthesize the technical components of data analysis into reports, presentations, and visual dashboards that are meaningful for the intended audience and deliver those components in a coherent, convincing format.

In the competitive business world, using data to its best advantage becomes all the more crucial. In this course, students will learn how to discern the levels of relevancy of data and the impact it has on operations as well as hone their ability to identify macro and micro level risk and evaluate risk management programs, policies, and strategies.

Building on prior coursework in decision methods and modeling, students will get a deeper understanding of the art and science of predictive analysis. Students will examine the elements that contribute to building reliable predictive models that result in actionable performance predictions such as identifying the variables that have the most predictive power and developing and deploying predictive models currently in use.

This course will emphasize the employment of advanced analytic strategies over the entire life cycle of the data analysis process. Using a comprehensive case-studies approach, students will logically extend and add definition to their existing analytic skill set, resulting in the development of a project proposal that will serve as preparation for the capstone experience.

This capstone course is the culminating experience for the M.S. in Data Analytics program. The aim of the capstone is to assess students' ability to synthesize and integrate the knowledge and skills they have developed throughout their coursework, rather than introducing new concepts. This course is structured to support student success in fulfilling program requirements.