Online Advanced Analytics in Higher Education Graduate Certificate
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The graduate certificate in Advanced Analytics in Higher Education at ASU Online prepares professionals to conduct advanced analytics and assist university personnel in making data-driven decisions for higher and postsecondary education.
As a leader in using reporting and analytics to enable data-driven decision making, ASU is uniquely positioned to provide a graduate certificate addressing the advanced analytics needed to support a broad range of institutional issues and needs. In this online certification program, the coursework stresses application of theory in practice to address topics including application of data mining, predictive analytics, sentiment analysis and statistical techniques as well as data management and security, data visualization and contextualization within higher education. Through real-world projects that provide opportunities to work with large datasets, students apply knowledge and skills of advanced analytic techniques. This program consists of six courses for a total of 15 credits.
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Mary Lou Fulton Teachers College brings people and ideas together to increase the innovation capabilities of individual educators, schools and organizations, districts, and communities. We know educators are the startup engines of individual minds and society.
ASU is ranked No. 11 in the world in the subject area of education according to the Academic Ranking of World Universities. MLFTC is ranked #16 according to the US News and World Report 2020 survey of graduate colleges of education in the US.
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.
The faculty of Mary Lou Fulton Teachers College creates knowledge by drawing on a range of academic disciplines including cognitive science, sociology and psychology, gaining insight into important questions about student experience and outcomes. The faculty is committed to connecting research to schools and other learning environments. Our teacher and leadership preparation programs combine scholarly rigor with practical application.
Graduates with an ASU Online advanced analytics in higher education graduate certificate have career opportunities illustrated in the following list. Career examples include but are not limited to:
Applicants must fulfill the requirements of both the Graduate College and the Mary Lou Fulton Teachers College.
Applicants are eligible to apply if they have earned a minimum of a bachelor's degree (business, economics, education, psychology or equivalent) or master's degree from a regionally accredited institution. To be considered for admission, your undergraduate or post-graduate coursework must include at least one semester of college-level statistical methods, including inferential statistics and regression analysis.
Applicants must have a minimum of a 3.00 cumulative GPA (scale is 4.00 = "A") in the last 60 hours of a student's first bachelor's degree program, or applicants must have a minimum of 3.00 cumulative GPA (scale is 4.00 = "A") in an applicable master's degree program.
Applicants are required to submit:
- Graduate admission application and application fee
- Official transcripts
- Personal statement describing the applicant's interests and reason for seeking this certificate
- Proof of English proficiency
Additional Application Information
An applicant whose native language is not English (regardless of current residency) must provide proof of English proficiency.
Applicants are required to have at least one year working in an education-related field, and have taken at least one graduate or undergraduate statistics course, finishing with a grade of "B" (scale is 4.00 = "A") or better.
View the Teacher's College Graduate Program Application Deadlines