We used Greenplum MPP, Apache MADlib and PL/Python libraries to analyze a variety of data sources pertaining to academic programs of students at a large public university to determine the factors that could be driving their success and retention.
One educational institution set out on a course to understand student success better and build new relationships with students based on data science. Ultimately, with the goal of improving retention and graduation rates, developing a more pro-active relationship with students to help them be more successful during and after graduation. By pursuing programs with an impact on these metrics, the educational institution would be in a better competitive position and improve the value of their education. For entities and companies across many industries, this is the journey to becoming a truly data-driven business where information is used to predict and affect business outcomes before they happen.
You can read more about this in the blog we published on Pivotal: Big Data in Education: Analyzing Student Clusters to Influence Success and Retention