Quantitative Methods / Research > STUDY GUIDE > ISYE 6501 Midterm 1 Week 1-11 Latest Update (All)
ISYE 6501 Midterm 1 Week 1-11 Latest Update
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Life is full of mysteries. Although that can feel a bit overwhelming at times, the interesting thing is that we can use math to explain a lot of what we see as the "unknown." In fact, that's the go... al of the field of analytics. Rather than looking at our businesses or organizations and wondering what will work and what won't, we can use analytics to sift through our data to explain why something happened or why one idea will work while another won't. If you're interested in learning more about how that works, you're in the right place. In this post, I go through the content in week 1 of ISYE 6501 to make sense of what analytics is and how we can use simple machine learning models to make better decisions. Why Analytics? We can use analytics to answer important questions, and we can break those questions down into three types: ● Descriptive Questions: What happened? What effect does spin rate have on how hard someone hits the ball? Which teachers in the school produce the best exam results? ● Predictive Questions: What will happen? How much will the global temperature increase in the next 100 years? Which product will be most popular? ● Prescriptive Questions: What action(s) would be best? When and where should firefighters be placed? How many delivery drivers should the pizza shop have on hand on certain days and times? In short, we can use analytics to make sense of the world around us and to make better decisions in a complex world. And we do this through something called modeling. Modeling is a way to mathematically explain a real-world situation so that we can understand why something happened (or will happen) and what we can do about it, but people often use the word 'modeling' to mean three different things: ● A real-life situation expressed as math ● Analyze the math ● Turn math analysis back to real-life solution To give an example of how the word 'model' can [Show More]
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