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Principles of Data Science
Chapter 1
What Are Data and Data Science?
Chapter Review
[1.1, LO 1.1.1, 1.1.2]
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
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
insights found from the analysis.
Solution: b. Data preparation: have the collected data stored in a server as is so that you can
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.
[1.1, LO 1.1.3]
2. Which of the following best describes the evolution of data management in the data science
process?
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.
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,
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.
11/11/24 For more free, peer-reviewed, openly licensed resources visit OpenStax.org. 2
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Principles of Data Science
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
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]
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
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
genetic basis of diseases
d. A chemist synthesizing new compounds in a laboratory
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
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.
Critical Thinking
[1.3, LO 1.3.4]
1. For each dataset, list the attributes.
a. Spotify dataset
b. CancerDoc dataset
Solution a: Following is the list of attributes in the Spotify dataset:
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).
11/11/24 For more free, peer-reviewed, openly licensed resources visit OpenStax.org. 3
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Principles of Data Science
[1.3, LO 1.3.3]
2. For each dataset, define the type of the data based on following criteria and explain why:
● Numeric vs. categorical
● If it is numeric, continuous vs. discrete; if it is categorical, nominal vs. ordinal
a. “artist_count” attribute of Spotify dataset
b. “mode” attribute of Spotify dataset
c. “key” attribute of Spotify dataset
d. the second column in CancerDoc dataset
Solution a: “artist_count” are integers that indicate the number of times that each track was
played. Thus, it is a numeric and discrete type of data since the count can only be integers.
Solution b: “mode” has only two string values—“Major” and “Minor”—so it is categorical.
There is no notion of ordering, so the data is nominal.
Solution c: “key” is a string attribute that has a finite set of values (e.g., “A”, “F”, “F#”), so it is
categorical data. It can be either nominal or ordinal depending on how a data scientist wants to
treat this data. If they want to consider ordering notion with respect to pitch (e.g., G is higher
than C), they can argue it is ordinal. If they simply want to treat the different kinds of pitch
without any ordering notion, they can argue it is nominal.
Solution d: The second column of the CancerDoc dataset indicates the type of cancer of each
entry in a string. There is a finite number of cancers in the dataset (thyroid, colon, and lung) and
these categories have no notion of ordering, so it is a categorical and nominal data type.
[1.3, LO 1.3.2]
3. For each dataset, identify the type of the dataset—structured vs. unstructured. Explain why.
a. Spotify dataset
b. CancerDoc dataset
Solution a: The Spotify dataset is a structured dataset since each item in the dataset is in a
same form.
Solution b: The CancerDoc dataset is an unstructured dataset since the third column is the main
information while the first and second columns serve as labels of each entry (i.e., used to
distinguish each item in the dataset). The third column is a free-form text, so this dataset is
unstructured.
[1.3, LO 1.3.4]
4. For each dataset, list the first data entry.
a. Spotify dataset
b. CancerDoc dataset
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Principles of Data Science
Solution a: The first entry shows up on the first row on each CSV file unless the top row is a
header row that indicates the names of different attributes in the dataset.
The first row on spotify-2023.csv is a header row. Therefore, the first item is located on the
second row of the CSV file. That is:
“Seven (feat. Latto) (Explicit Ver.)”, “Latto, Jung Kook”, 2, 2023, 7, 14, 553, 147,
141381703, 43, 263, 45, 10, 826, 125, “B”, “Major”, 80, 89, 83, 31, 0, 8, 4
Solution b: The first entry shows up on the first row on each CSV file unless the top row is a
header row that indicates the names of different attributes in the dataset. The first row on
CancerDoc.csv is a header row. Therefore the first item is located on the second row of the CSV
file. That is:
0, “Thyroid_Cancer”, “Thyroid surgery in children…”
Note that the last attribute value is abbreviated with … since it is a very long string.
[1.3, LO 1.3.4]
5. Open the WikiHow dataset (ch1-wikiHow.json) and list the attributes of the dataset.
Solution: The ch1-wikiHow.json file has a list of items in an array (i.e., [ ]). Each array has an
object (i.e., { }) in which there are nine attributes total. The attributes are: “Time”, “URL”,
“MainTask”, “MainTaskSummary”, “Steps”, “Categories”, “Ingredients”, “Requirements”, and
“Tips”.
Note that some attributes have data in the form of an array as well. For example, “Steps” is an
array of which each element is also an object with three fields—“Headline”, “Description”, and
“Links”.
[1.5, LO 1.5.3]
6. Draw scatterplot between bpm (x-axis) and danceability (y-axis) of the Spotify dataset using:
a. Python Matplotlib
b. A spreadsheet program such as MS Excel or Google Sheets (Hint: Search “Scatterplot”
on Help.)
Solution a: The following code draws the same scatterplot. Note the Python-generated plot will
not have a title.
import matplotlib.pyplot as plt
plt.scatter(data["bpm"], data["danceability_%"]) # draw the
scatterplot
plt.show()
11/11/24 For more free, peer-reviewed, openly licensed resources visit OpenStax.org. 5
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Principles of Data Science
Chapter 1
What Are Data and Data Science?
Chapter Review
[1.1, LO 1.1.1, 1.1.2]
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
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
insights found from the analysis.
Solution: b. Data preparation: have the collected data stored in a server as is so that you can
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.
[1.1, LO 1.1.3]
2. Which of the following best describes the evolution of data management in the data science
process?
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.
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,
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.
11/11/24 For more free, peer-reviewed, openly licensed resources visit OpenStax.org. 2
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Principles of Data Science
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
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]
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
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
genetic basis of diseases
d. A chemist synthesizing new compounds in a laboratory
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
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.
Critical Thinking
[1.3, LO 1.3.4]
1. For each dataset, list the attributes.
a. Spotify dataset
b. CancerDoc dataset
Solution a: Following is the list of attributes in the Spotify dataset:
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).
11/11/24 For more free, peer-reviewed, openly licensed resources visit OpenStax.org. 3
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Principles of Data Science
[1.3, LO 1.3.3]
2. For each dataset, define the type of the data based on following criteria and explain why:
● Numeric vs. categorical
● If it is numeric, continuous vs. discrete; if it is categorical, nominal vs. ordinal
a. “artist_count” attribute of Spotify dataset
b. “mode” attribute of Spotify dataset
c. “key” attribute of Spotify dataset
d. the second column in CancerDoc dataset
Solution a: “artist_count” are integers that indicate the number of times that each track was
played. Thus, it is a numeric and discrete type of data since the count can only be integers.
Solution b: “mode” has only two string values—“Major” and “Minor”—so it is categorical.
There is no notion of ordering, so the data is nominal.
Solution c: “key” is a string attribute that has a finite set of values (e.g., “A”, “F”, “F#”), so it is
categorical data. It can be either nominal or ordinal depending on how a data scientist wants to
treat this data. If they want to consider ordering notion with respect to pitch (e.g., G is higher
than C), they can argue it is ordinal. If they simply want to treat the different kinds of pitch
without any ordering notion, they can argue it is nominal.
Solution d: The second column of the CancerDoc dataset indicates the type of cancer of each
entry in a string. There is a finite number of cancers in the dataset (thyroid, colon, and lung) and
these categories have no notion of ordering, so it is a categorical and nominal data type.
[1.3, LO 1.3.2]
3. For each dataset, identify the type of the dataset—structured vs. unstructured. Explain why.
a. Spotify dataset
b. CancerDoc dataset
Solution a: The Spotify dataset is a structured dataset since each item in the dataset is in a
same form.
Solution b: The CancerDoc dataset is an unstructured dataset since the third column is the main
information while the first and second columns serve as labels of each entry (i.e., used to
distinguish each item in the dataset). The third column is a free-form text, so this dataset is
unstructured.
[1.3, LO 1.3.4]
4. For each dataset, list the first data entry.
a. Spotify dataset
b. CancerDoc dataset
11/11/24 For more free, peer-reviewed, openly licensed resources visit OpenStax.org. 4
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Principles of Data Science
Solution a: The first entry shows up on the first row on each CSV file unless the top row is a
header row that indicates the names of different attributes in the dataset.
The first row on spotify-2023.csv is a header row. Therefore, the first item is located on the
second row of the CSV file. That is:
“Seven (feat. Latto) (Explicit Ver.)”, “Latto, Jung Kook”, 2, 2023, 7, 14, 553, 147,
141381703, 43, 263, 45, 10, 826, 125, “B”, “Major”, 80, 89, 83, 31, 0, 8, 4
Solution b: The first entry shows up on the first row on each CSV file unless the top row is a
header row that indicates the names of different attributes in the dataset. The first row on
CancerDoc.csv is a header row. Therefore the first item is located on the second row of the CSV
file. That is:
0, “Thyroid_Cancer”, “Thyroid surgery in children…”
Note that the last attribute value is abbreviated with … since it is a very long string.
[1.3, LO 1.3.4]
5. Open the WikiHow dataset (ch1-wikiHow.json) and list the attributes of the dataset.
Solution: The ch1-wikiHow.json file has a list of items in an array (i.e., [ ]). Each array has an
object (i.e., { }) in which there are nine attributes total. The attributes are: “Time”, “URL”,
“MainTask”, “MainTaskSummary”, “Steps”, “Categories”, “Ingredients”, “Requirements”, and
“Tips”.
Note that some attributes have data in the form of an array as well. For example, “Steps” is an
array of which each element is also an object with three fields—“Headline”, “Description”, and
“Links”.
[1.5, LO 1.5.3]
6. Draw scatterplot between bpm (x-axis) and danceability (y-axis) of the Spotify dataset using:
a. Python Matplotlib
b. A spreadsheet program such as MS Excel or Google Sheets (Hint: Search “Scatterplot”
on Help.)
Solution a: The following code draws the same scatterplot. Note the Python-generated plot will
not have a title.
import matplotlib.pyplot as plt
plt.scatter(data["bpm"], data["danceability_%"]) # draw the
scatterplot
plt.show()
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