90% of data modeling is data cleansing. Here is a sample done with Python

by Michael

This is a particularly hairy example of comparative program performance that needed to take into account: (a) program consolidations, (b) shifting departmental oversight, (b) school name changes, (c) sliding test score ranges, (d) and a host of other data idiosyncrasies. This code takes a days-long manual cleaning process and reduces it to about 5 minutes. It’s a non-destructive process that maintains the original file. Additionally, the entire process is documented within the code.

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Dr. Mike Urmeneta is an award-winning researcher, educator, data scientist, and storyteller with a passion for helping institutions improve and succeed through agile and collaborative approaches to research and analysis. He has extensive experience working with universities and has been recognized for his work by leading organizations such as the Association for Institutional Research, the National Association of College and University Business Officers, and EDUCAUSE. His diverse background and ability to build strong relationships with a variety of stakeholders have allowed him to make a significant impact on institutional policies, procedures, and priorities. Most recently, as an instructor for AIR’s Data Literacy Institute, he has been preparing leadership teams to embrace a culture of data-informed decision-making. Prior to this role, he served as the director of analytics and business intelligence for the New York Institute of Technology, providing strategic guidance to various departments, the president’s office, and the board of trustees. Dr. Urmeneta has also held various administrative roles at New York University, including in admissions, financial aid, enrollment and retention, alumni relations, and development. He holds a Bachelor of Science in Mechanical Engineering and a Master of Science in Management from NYU, and a Doctor of Education degree from Northeastern University, where he received the Dean’s Medal for Outstanding Doctoral Work for his research on first-generation college students.