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Graduate Certificate

Why ASU Online?• Convenient 7.5 week courses with flexible start dates• Award-winning faculty• Earn the same degree as students attending on campus• Personalized support system throughout your certificate program• No out-of-state tuition

Mary Lou Fulton Teachers College

Advanced Analytics in Higher Education

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A world class university. 100% online.The graduate certificate in Advanced Analytics in Higher Education prepares professionals to conduct the advanced analytics needed to support data-driven decision making in all academic and operational areas of higher and postsecondary education.

In this online certificate program, the coursework addresses the application of data mining, predictive analytics, sentiment analysis, and data visualization techniques to higher education-specific problems with the goal of preparing individuals at all levels to use data in their decision-making processes. Through real-world projects using top tools such as Tableau and RapidMiner, students apply the knowledge and skills learned while gaining an understanding of the security and ethical considerations of analytics.

Program Courses

HED 605: Data Management & Preparation for Higher Education Analytics

HED 603: Foundation of Data Analytics in Higher Education

HED 604: Analytics Process & Technology in Higher Education

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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? 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 spectrums of administration.

A successful analytic project 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, and teaches a repeatable, industry-standard process for managing an analytic project. It further explains why it is important to keep documentation on each step and 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.

HED 606: Advanced Analytic Methods for Higher Education

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 differnt models and evaluate the output of those models in order to choose the one that best informs decision-making.

HED 607: Visualization & Presentation for Higher Education

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 on 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.

HED 608: Trends in Advanced Analytic Methods for Higher Education

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.