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ISYE 6501 Introduction to Analytics Modeling Final Exam Questions and Answers Study Guide

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Comprehensive study and review resource for ISYE 6501 – Introduction to Analytics Modeling at Georgia Institute of Technology. This material is designed to help students review essential analytics modeling concepts, including regression, classification, optimization, simulation, clustering, decision trees, forecasting, machine learning fundamentals, model selection, data visualization, predictive analytics, and business decision-making. It serves as a structured revision companion for reinforcing core analytics knowledge and preparing for the final examination while studying alongside official Georgia Tech course materials. ISYE 6501 introduces the fundamental models and techniques commonly used in analytics and the modeling process.

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Institution
Data Science
Course
Data Science

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ISYE 6501 - Final Exam
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Terms in this set (414)



What do descriptive questions ask? What happened? (e.g., which customers are most alike)


What do predictive questions ask? What will happen? (e.g., what will Google's stock price be?)


What do prescriptive questions ask? What action(s) would be best? (e.g., where to put traffic lights)




What is a model? Real-life situation expressed as math.


What do classifiers help you do? differentiate


What is a soft classifier and when is it used? In some cases, there won't be a line that separates all of the labeled
examples. So
we use a classifier that minimizes the number of mistakes.

What does it mean when the classifier/decision boundary The horizontal attribute is all
that is needed. is almost parallel to the vertical x-axis?

,What does it mean when the classifier/decision boundary The vertical attribute is all that is
needed. is almost parallel to the horizontal y-axis?


What is time-series data? The same data recorded over time often recorded at equal intervals


What is quantitative data? Number with a meaning: higher means more, lower means less (e.g.,
age, sales, temperature, income)


What is categorical data? Numbers w/o meaning (e.g., zip codes), non-numeric (e.g., hair color),
binary data (e.g., male/female, yes/no, on/off)




Which of these is time series data? A
A. The average cost of a house in the United
States every year since 1820
B. The height of each professional basketball
player in the NBA at the start of the season




Which of these is structured data? B
A. The contents of a person's Twitter feed
B. The amount of money in a person's bank account

, What is structured data? Data that can be stores in a structured way


What is unstructured data? Data that is not easily described and stored (e.g., written text)


A survey of 25 people recorded each person's family size A.
and type of car. Which of these is a data point? A data point is all the information about one observation
A. The 14th person's family size and car type
B. The 14th person's family size
C. The car type of each person


The farther the wrongly classified point is from the line ___ The bigger the mistake we've made




The term including the margin gets larger so the As lambda gets
larger importance of a large margin out weights avoiding
mistakes and classifying known data samples.




That term also drops towards zero, so the importance of As lambda drops
towards zero minimizing mistakes and classifying known data points
outweighs having a large margin.

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Uploaded on
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