GTx: ISYE6501x Introduction to Analytics Modeling Midterm
Quiz 1 & QUIZ 2 - GT Students and Verified MM Learners latest
2023
Why Analytics? - ANSWER: -We can use analytics to answer important questions.
-We can use analytics to make sense of the world around us and to make better
decisions in a complex world.
Three types of questions - ANSWER: -descriptive
-predictive
-prescriptive
Descriptive Questions - ANSWER: What happened?
ex.
-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 - ANSWER: What will happen?
ex.
-How much will the global temperature increase in the next 100 years?
-Which product will be most popular?
Prescriptive Questions - ANSWER: What action(s) would be best?
ex.
-When and where should firefighters be placed?
-How many delivery drivers should the pizza shop have on hand on certain days and
times?
Modeling - ANSWER: 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.
Data Table - ANSWER: A display of information in a grid-like format of rows and
tables
Row - ANSWER: Contains the record or data of the columns
Column - ANSWER: Contains the name, data type, and any other attribute of the
data
Structured Data - ANSWER: Data that has a defined length, type, and format and
includes numbers, dates, or strings such as Customer Address.
Unstructured Data - ANSWER: Data that does not have a defined length and is not
easily stored or described. (ex. text from social media)
, Quantitative Data - ANSWER: Numbers with a meaning (ex. 3 baseballs)
Categorical Data - ANSWER: Numbers without meaning (an area code or country of
origin)
Binary Data - ANSWER: Data that takes one of two values (yes or no)
Unrelated Data - ANSWER: No relationship between data points (players on different
teams)
Time Series Data - ANSWER: Same data recorded over time (athletes performance
over time)
Scaling Data - ANSWER: Transforming your data so that features are within a specific
range (i.e. 0-1)
Standardizing Data - ANSWER: Change your observations so they can be described as
a normal distribution
Validation - ANSWER: Verifying that models are performing as intended
Classification - ANSWER: The process of grouping things based on their similarities
Hard Classifier - ANSWER: Classifies into groups perfectly
Soft Classifier - ANSWER: Gives as good of a separation as possible
If classifier line is vertical, which variable matters? - ANSWER: Variable on the x-axis
If classifier line is horizontal, which variable matters? - ANSWER: Variable on the y-
axis
Support Vector Machine - ANSWER: Supervised machine learning models used for
classification. Their goal is to maximize (or optimize) the space between the support
vectors to minimize errors between the classes.
Support Vector - ANSWER: In SVM models, the closest point to the classifier, among
those in a category.
In SVM, if one feature is much bigger than the other (x1 = .3-.6 and x2 = 1000-2000)
what will happen to the model? - ANSWER: The large range (x2) will dominate the
model and throw off our results. Scaling is needed.
K-nearest neighbor (KNN) - ANSWER: Algorithm used to classify data.
-rather than using a line to separate data into classes, KKN classifies data by looking
at a data points "nearest neighbors"
Quiz 1 & QUIZ 2 - GT Students and Verified MM Learners latest
2023
Why Analytics? - ANSWER: -We can use analytics to answer important questions.
-We can use analytics to make sense of the world around us and to make better
decisions in a complex world.
Three types of questions - ANSWER: -descriptive
-predictive
-prescriptive
Descriptive Questions - ANSWER: What happened?
ex.
-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 - ANSWER: What will happen?
ex.
-How much will the global temperature increase in the next 100 years?
-Which product will be most popular?
Prescriptive Questions - ANSWER: What action(s) would be best?
ex.
-When and where should firefighters be placed?
-How many delivery drivers should the pizza shop have on hand on certain days and
times?
Modeling - ANSWER: 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.
Data Table - ANSWER: A display of information in a grid-like format of rows and
tables
Row - ANSWER: Contains the record or data of the columns
Column - ANSWER: Contains the name, data type, and any other attribute of the
data
Structured Data - ANSWER: Data that has a defined length, type, and format and
includes numbers, dates, or strings such as Customer Address.
Unstructured Data - ANSWER: Data that does not have a defined length and is not
easily stored or described. (ex. text from social media)
, Quantitative Data - ANSWER: Numbers with a meaning (ex. 3 baseballs)
Categorical Data - ANSWER: Numbers without meaning (an area code or country of
origin)
Binary Data - ANSWER: Data that takes one of two values (yes or no)
Unrelated Data - ANSWER: No relationship between data points (players on different
teams)
Time Series Data - ANSWER: Same data recorded over time (athletes performance
over time)
Scaling Data - ANSWER: Transforming your data so that features are within a specific
range (i.e. 0-1)
Standardizing Data - ANSWER: Change your observations so they can be described as
a normal distribution
Validation - ANSWER: Verifying that models are performing as intended
Classification - ANSWER: The process of grouping things based on their similarities
Hard Classifier - ANSWER: Classifies into groups perfectly
Soft Classifier - ANSWER: Gives as good of a separation as possible
If classifier line is vertical, which variable matters? - ANSWER: Variable on the x-axis
If classifier line is horizontal, which variable matters? - ANSWER: Variable on the y-
axis
Support Vector Machine - ANSWER: Supervised machine learning models used for
classification. Their goal is to maximize (or optimize) the space between the support
vectors to minimize errors between the classes.
Support Vector - ANSWER: In SVM models, the closest point to the classifier, among
those in a category.
In SVM, if one feature is much bigger than the other (x1 = .3-.6 and x2 = 1000-2000)
what will happen to the model? - ANSWER: The large range (x2) will dominate the
model and throw off our results. Scaling is needed.
K-nearest neighbor (KNN) - ANSWER: Algorithm used to classify data.
-rather than using a line to separate data into classes, KKN classifies data by looking
at a data points "nearest neighbors"
