Online Advanced Analytics in Higher Education Graduate Certificate

How are decisions made in university management? How do institutions use data analytics to inform their decisions? How can we improve the use of data analytics in decision-making? These questions, and a multitude of similar thoughts, are central to the content of this class. This course aims to provide participants with insights into the functioning of data analytics in decision-making at higher education institutions by exposing them to theoretical explanations of decision-making as well as various examples of data and analytics that support different spectrum of administration.

A successful analytic process requires a repeatable, documentable process that uses the right technology for the institution’s question. This course teaches the student how to properly define a question, identify the appropriate types of analytic tools for the question (as well as those that are not), and teaches a repeatable, industry-standard process for managing an analytic process. It further explains why it is important to keep documentation on each stage and provides best-practice techniques for doing so.

Data-informed decision making only works if the right data is used, prepared correctly and managed carefully. This course examines the types of data commonly available in higher education, how it can be accessed, guidelines for preparing the data for analysis and best practices around storing, securing and managing multiple data sets.

Data-informed decision making often requires analysis that is more in-depth, complex and sophisticated than can be done in a spreadsheet. Predictive analytics in particular defy simple tools and approaches, yet show substantial promise in improving student outcomes and operational efficiency with universities. This course will teach participants the skills necessary to perform more advanced analytic procedures and provide a conceptual understanding of how the procedures work. Using Rapidminer, students will configure several different models and evaluate the output of those models in order to choose the one that best informs decision-making.

An analysis, regardless of how complex, is only as good as the analyst’s ability to explain it to decision-makers. This course is focused the process of creating visualizations and tables that clearly communicate. We will discuss the principles of effective visualization, the guidelines for creating a table or graph that are accurate and clear, and the use of visualization in the analytic process. Using Tableau, students will create a variety of different visualizations and integrate them into presentations ready for senior leadership.

General data mining techniques are useful, but certain applications have proven of particular interest to higher education practitioners. This course will introduce those topics, the principle literature around them including discussion of their efficacy in improving higher education outcomes, and future technological advancements to watch.