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Sports Analytics: Putting The Fun Back Into Analytics, Walt Degrange
Sports Analytics: Putting The Fun Back Into Analytics, Walt Degrange
Operations Management Presentations
With the recent success of sports teams heavily using analytics (Dodgers, Patriots, Capitals, Warriors, Leicester City F.C.), does this mean that analytics has gained a foothold in the sports world? I use a k-means clustering model to determine if performance since 2015 in the four major US sports can support this question. And is there a career path that a high school student can use to become a sports analytics professional? This presentation attempts to answer that question by exploring all the areas of the application of analytics in sports. The final point the brief makes is that by using …
Characterizing Statin Use Among Prediabetic Patients With Predictive Analytics, Alexandra Gentile
Characterizing Statin Use Among Prediabetic Patients With Predictive Analytics, Alexandra Gentile
Industrial Engineering Undergraduate Honors Theses
Diabetes is one of the leading causes of death in the United States and can cause severe impairments to those diagnosed. Prediabetes is a state when a patient has higher fasting plasma glucose levels than a non-diabetic person but is not quite high enough to be considered diabetes. Both diabetic and prediabetic patients are at higher risk for cardiovascular diseases (CVD), which is the leading cause of death in the United States. The primary form for prevention and treatment of CVD is through statin therapy. Statins are a class of medications used to treat and prevent CVD by limiting cholesterol …
Machine Learning In Manufacturing: Review, Synthesis, And Theoretical Framework, Ajit Sharma, Zhibo Zhang, Rahul Rai
Machine Learning In Manufacturing: Review, Synthesis, And Theoretical Framework, Ajit Sharma, Zhibo Zhang, Rahul Rai
Business Administration Faculty Research Publications
There has been a paradigmatic shift in manufacturing as computing has transitioned from the programmable to the cognitive computing era. In this paper we present a theoretical framework for understanding this paradigmatic shift in manufacturing and the fast evolving role of artificial intelligence. Policy, Strategic and Operational implications are discussed. Implications for the future of strategy and operations in manufacturing are also discussed. Future research directions are presented.