Test Bank — Data Science For All, 1st Edition (Davis, 2025), Chapters 1-10 | All Chapters Covered

TEST BANK
Data Science for All
Brennan Davis and Hunter Glanz
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1st Edition
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, TABLE OF CONTENTS
Data Science For All (1st Edition)
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Brennan Davis and Hunter Glanz
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Chapter 1 What Is Data Science?
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Chapter 2 Data Wrangling: Preprocessing
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Chapter 3 Making Sense of Data Through Visualization
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Chapter 4 Exploratory Data Analysis
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Chapter 5 Data Management
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Chapter 6 Understanding Uncertainty, Probability, and Variability
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Chapter 7 Drawing Conclusions from Data
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Chapter 8 Machine Learning
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Chapter 9 Supervised Learning
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Chapter 10 Unsupervised Learning
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,Data Science for All, 1e (Glanz/Davis)
Chapter 1 What is Data Science?

1.1 Introduction to Data Science
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1 Define terms relating to data science.
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1) Which of the following best describes the interdisciplinary nature of data science?
A) Data science combines statistics, computer science, and domain expertise to extract
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knowledge from data.
B) Data science is solely focused on statistical analysis.
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C) Data science is primarily about data visualization.
D) Data science only involves machine learning.
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Answer: A
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2) What are the three iterative components of the data science life cycle?
A) Data preparation, analysis, and storytelling
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B) Data collection, visualization, and deletion
C) Data extraction, transformation, and archiving
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D) Data acquisition, modeling, and disposal
Answer: A
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2 Identify the scope of data science including its interdisciplinary nature or life cycle.
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1) Which term is defined as the process of collecting, cleaning, transforming, integrating, and
managing data for effective analysis and storytelling?
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A) Data preparation
B) Data visualization
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C) Data modeling
D) Data archiving
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Answer: A
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2) In the context of data science, what does "data storytelling" refer to?
A) Communicating data insights through summaries, visualizations, and narratives.
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B) Collecting and cleaning raw data.
C) Analyzing data to find patterns.
D) Storing data in a structured format.
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Answer: A
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, 3 Answer questions relating to the interdisciplinary nature or life cycle of data science.

1) Why is data science considered interdisciplinary?
A) It combines statistics, computer science, and domain expertise.
B) It focuses solely on statistical analysis.
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C) It is limited to computer science applications.
D) It only involves data visualization.
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Answer: A
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2) What process involves cleaning, transforming, and integrating data to make them useful for
analysis and storytelling?
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A) Data wrangling
B) Data visualization
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C) Data modeling
D) Data archiving
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Answer: A
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1.2 Data in Tables

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Answer questions about the relationship of observations and variables.

1) What is a Boolean variable type in data tables?
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A) A variable that contains only two possible values, TRUE or FALSE
B) A variable that takes on only numerical values
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C) A variable that can take on any text
D) A variable that describes a category
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Answer: A
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2) What is the relationship between an observational unit and a variable?
A) An observational unit is an entity about which data are recorded, and a variable is a recorded
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characteristic of that entity.
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B) A variable is an entity about which data are recorded, and an observational unit is a recorded
characteristic of that entity.
C) An observational unit is a subset of variables in a dataset.
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D) A variable is always a numerical value associated with an observational unit.
Answer: A
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2 Answer questions about tidying messy data.
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1) Considering the numbers 0, -2, 3.5, and 10, which one could be converted to a Boolean value
in a data analysis context?
A) 0
B) -2
C) 3.5
D) 10
Answer: A


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