Solutions Manual — Principles of Data Science, 1st Edition (Ault, Liao & Musolino, 2025), Chapters 1-10 | All Chapters Covered

SOLUTIONS MANUAL
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Principles of Data Science
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Shaun V. Ault, Soohyun Nam Liao, and Larry Musolino
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1st Edition
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TABLE OF CONTENTS
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Solutions Manual: Principles of Data Science, 1st Edition
By Shaun Ault, Soohyun Nam Liao, and Larry Musolino


Unit 1 Introducing Data Science and Data Collection
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Chapter 1 What Are Data and Data Science?

Chapter 2 Collecting and Preparing Data
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Unit 2 Analyzing Data Using Statistics

Chapter 3 Descriptive Statistics: Statistical Measurements and Probability Distributions
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Chapter 4 Inferential Statistics and Regression Analysis

Unit 3 Predicting and Modeling Using Data

Chapter 5 Time Series and Forecasting
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Chapter 6 Decision-Making Using Machine Learning Basics

Chapter 7 Deep Learning and AI Basics
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Unit 4 Maintaining a Professional and Ethical Data Science Practice

Chapter 8 Ethics Throughout the Data Science Cycle

Chapter 9 Visualizing Data
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Chapter 10 Reporting Results
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, Principles of Data Science
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Chapter 1
What Are Data and Data Science?
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Chapter Review
[1.1, LO 1.1.1, 1.1.2]
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1. Select the incorrect step and goal pair of the data science cycle.
a. Data collection: collect the data so that you have something for analysis.
b. Data preparation: have the collected data stored in a server as is so that you can start
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the analysis.
c. Data analysis: analyze the prepared data to retrieve some meaningful insights.
d. Data reporting: present the data in an effective way so that you can highlight the
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insights found from the analysis.


Solution: b. Data preparation: have the collected data stored in a server as is so that you can
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start the analysis.
Rarely is collected data already in good shape for analysis. Most of the time, collected data
needs to be processed to be suitable for the analysis of interest. An example of preparation can
be dealing with missing data—removing them or filling them.
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[1.1, LO 1.1.3]
2. Which of the following best describes the evolution of data management in the data science
process?
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a. Initially, data was stored locally on individual computers, but with the advent of cloud-
based systems, data is now stored on designated servers outside of local storage.
b. Data management has remained static over time, with most data scientists continuing to
store and process data locally on individual computers.
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c. The need for data management arose as a result of structured data becoming
unmanageable, leading to the development of cloud-based systems for data storage.
d. Data management systems have primarily focused on analysis rather than processing,
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resulting in the development of modern data warehousing solutions.


Solution: a. Initially, data was stored locally on individual computers, but with the advent of
cloud-based systems, data is now stored on designated servers outside of local storage.

, Principles of Data Science
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Data storage evolved from local to cloud-based systems for a variety of reasons, including
increasing complexity of data, security concerns, reliability, etc. Option b) fails to recognize the
evolution of data storage. Option c) incorrectly focuses on structured data as the sole reason for
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data storage solutions changing over time. Option d) incorrectly suggests that analysis of data
was a driving factor in the evolution of data storage.

[1.2, LO 1.2.1]
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3. Which of the following best exemplifies the interdisciplinary nature of data science in various
fields?
a. A historian traveling to Italy to study ancient manuscripts to uncover historical insights
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about the Roman Empire
b. A mathematician solving complex equations to model physical phenomena
c. A biologist analyzing a large dataset of genetic sequences to gain insights about the
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genetic basis of diseases
d. A chemist synthesizing new compounds in a laboratory
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Solution: c. A biologist analyzing a large dataset of genetic sequences to gain insights about the
genetic basis of diseases
Traditionally, biologists would conduct lab experiments to answer questions in their field;
however, nowadays data science is being used to analyze large datasets to extract valuable
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information that can shed light on complex topics such as the genetic basis of diseases. Option
a) is incorrect as studying primary sources does not inherently involve data science. Option b) is
incorrect as solving equations is not in the domain of data science. Option d) is incorrect as it
describes the traditional work of a chemist as a lab scientist.
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Critical Thinking
[1.3, LO 1.3.4]
1. For each dataset, list the attributes.
a. Spotify dataset
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b. CancerDoc dataset


Solution a: Following is the list of attributes in the Spotify dataset:
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track_name, artist(s)_name, artist_count, released_year, released_month, released_day,
in_spotify_playlists, in_spotify_charts, streams, in_apple_playlists, in_apple_charts,
in_deezer_playlists, in_deezer_charts, in_shazam_charts, bpm, key, mode, danceability_%,
valence_%, energy_%, acousticness_%, instrumentalness_%, liveness_%, speechiness_%
Solution b: The CancerDoc dataset has three attributes; however, none of these attributes have
a clear name. They are: the column with numeric identifiers (the first column), the column with
cancer type (the second column), and the actual text (the third column).