Data Science for Social Impact in Higher Education:  First Steps

Omar E. Hason

Undergraduate in Applied Statistics major at University of Illinois Chicago who completed the experimental module in STAT 385: Elementary Statistical Techniques for Machine Learning and Big Data

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The most valuable experience I believe gained from the STAT 385 final project was the ability to work in an uncontrolled environment. This project was assigned to my group and I with full creative freedom to analyze any problem we discovered when looking through the dataset. This was unusual to us as most of our University courses provided us with the problem we must solve, having the freedom to come up with our own problem was a unique challenge. Upon my group and I diving into the dataset, we chose to utilize machine learning techniques to analyze what metrics are significant in causing a fatality in a Chicago car crash. From there, we made new discoveries and answered our problem while doing so. The reason I believe us developing our own problem to solve was so beneficial is because this resembles real-world analysis closely. In the real world, we must constantly use our creative thinking in order to come up with new problems to be solved in order to push the technological curve and create new advancements in growing industries. I am grateful for the opportunity to have worked on a project like this one and will use the lessons it has taught me throughout my career.

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