by Angela Guess
David Kil and Mark Killiron recently broke down the vocabulary of data science on EdSurge.com for students, non-data professionals, and anyone interested in learning more about this burgeoning field. Their list of terms includes, “DESCRIPTIVE ANALYTICS: Examines historical data and identifies trends or patterns over time from known facts to inform future decisions. Why it matters: Descriptive analytics allow institutions to understand trends, such as enrollment, retention and course selection, and to use quantitative data analysis to understand the underlying factors that influence those outcomes.”
They go on, “PREDICTIVE ANALYTICS: Encompass multiple techniques to learn relationships between historical events and what happened subsequent to the historical events, so that such relationships can be used to predict future outcomes based on current events. Why it matters: Predictive analytics helps colleges and universities understand the unique challenges and opportunities for individual students, rather than just cohorts or trends, so that that they can identify the right supports and influence their trajectory.”
The list continues, “PRESCRIPTIVE ANALYTICS: Examine the relationship between descriptive analytics and predictive analytics to determine the best way to achieve a desired outcome. Why it matters: Prescriptive analytics inform the decision-making process, allowing institutions to weigh the impacts and effects of certain decisions that can lead to desired outcomes.”
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