Chapter 1: What Is Data Science?
Chapter Review Questions
Section 1. Introduction to Data Science
1. What is the interdisciplinary field that uses scientific methods to extract knowledge from data?
Data science.
2. What is the primary purpose of data analysis in data science?
To generate insights and inform decision-making.
3. Define a population in the context of data science.
A population is the set of every and all items or individuals that are of interest for a question.
4. What other field, often confused with data science, applies data science principles to domain-specific
problems?
Data analytics.
5. What are some of the modern challenges that have shaped data science?
Increased complexity of data and analysis methods, as well as rapidly increasing amounts of the available
data.
6. What constitutes a sample in data science?
A subset of the population.
7. In the context of data science, what is the significance of understanding samples and populations?
To inform better decision-making in society.
8. What combination of fields does data science encompass?
Statistics, computer science, and domain knowledge.
9. When was the term “data science” first proposed as an alternative for “computer science”?
1974
10. What is the role of a data scientist in the context of the data science lifecycle?
To extract actionable insights from data.
11. What is the most significant reason for the importance of data and their analysis, according to the text?
To describe and draw conclusions about a population based on the data collected for a sample.
Section 2: Data in Tables
12. In data science, what are the entities about which data are recorded called?
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© 2026
,Data Science for All
Observational units
13. What are the recorded characteristics of observational units called?
Variables of interest
14. What term is used to refer to the data for a single observational unit?
An observation
15. From the following set of numbers: 0.0, 2.71828, -3, 5, which one is most likely to be stored as a float
variable?
2.71828
16. Which type of variable is used to designate categories?
Categorical
17. In the context of data science, what does it mean for data to be tidy?
When data is tidy, it is structured so that each column represents a distinct variable, each row corresponds
to a unique observation, and each cell holds only one value.
18. What do we call the form when data are represented as a table with rows and columns?
Tabular form
19. Integers and floats are examples of what kind of variables?
Quantitative
20. What kind of variables only take on values of TRUE and FALSE, sharing aspects of both categorical and
quantitative variables?
Boolean
21. What form of quantitative variables consists only of umbers with no decimal points?
Integer
22. Given the numbers 5000, -2.01, 1.3, and 1/2, which one is most likely to be stored as an integer variable?
5000
23. What form of quantitative variables consists of numbers that may have decimal points?
Float
24. Given the set of numbers 0, 2, 5, and 1.5, which is most likely to be converted to a Boolean value in a data
analysis context?
0
25. What form of categorical variables contains words or characters without order or numerical utility?
Text
26. Given the numbers -1.0, 0.0, 3, and 4, which one could be stored as a float or converted to a Boolean
variable?
0.0
27. Given the numbers 2.71828, -5, 3.14, and 2.01, which one is most likely to be stored as an integer in a data
structure?
-5
28. What is metadata?
Data about data.
1
© 2026
, Chapter 1: What Is Data Science?
29. When is data considered messy?
When it is not yet organized in a well-defined structure.
30. Which of the following numbers is likely to be stored as a float variable in a data set: 1, 4.44, 144, or 0?
4.44
31. When data are in tabular form, what is stored in the columns? What is stored in the rows?
The variables are stored in the columns, and the observations are stored in the rows.
32. When the rows and columns of tabular data are fully populated, and each cell of the table corresponds to a
single value, we consider the dataset clean and organized. What is the term we use to describe this?
Tidy
33. Given the numbers 1243424, 1.2, -1.01, and 2.03, which is most likely to be stored as an integer variable?
1243424
34. What is the primary role of a data key or codebook?
To store metadata
35. In a tidy dataset, what does each column represent?
A variable
Section 3: Data Preparation
36. In the context of data science, what does it mean to prepare data?
Accessing, cleaning, transforming, and integrating data.
37. What is the first step in data wrangling?
Clean the data to ensure its quality.
38. For what purpose is data transformation essential?
Organizing and formatting data for efficient analysis.
39. What is the goal of integrating datasets?
To combine two or more sources for richer insights.
40. In the context of data preparation, what does data wrangling refer to?
Converting raw data into formats necessary for analysis.
41. Describe the process of data collection.
Gathering data systematically from various sources.
42. What is the significance of data quality and relevance in data collection?
To form the foundation of analysis and insights.
43. What does the term “unstructured data” mean?
Data that are not organized in predefined formats.
44. What is the process of organizing data (often into tables) called?
Data structuring.
Section 4: Data Analysis and Storytelling
45. What is descriptive analysis in data science is primarily concerned with?
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© 2026
, Data Science for All
Reporting what has happened.
46. What is the goal of diagnostic analytics in data science?
To explain why something might have happened.
47. What is the goal of predictive analysis in data science?
To estimate future behaviors based on past data.
48. What is the component of data storytelling that allows us to communicate statistics and analysis results in
form of graphs and charts?
Visualization
49. Describe prescriptive analysis in the context of data science.
The recommendation of actions based on predictive analysis.
50. What is the purpose of communication, in the context of data science?
To make information more accessible and guide decision-making.
51. How is data visualization useful in data science?
Answers may vary. Example: Data visualization is useful because it allows the data scientist to show
information concisely in a graphical format, which facilitates storytelling.
52. What is the ultimate goal of data science storytelling?
To effectively communicate analysis results to a wider audience and optimize decision-making across
various fields.
53. What does effective storytelling in data science require?
The ability to replicate results based on the process description.
Section 5: Data Science in Society and Industry
54. What is the primary driver for the growth of data science in society and industry?
B. The increasing amounts of available data.
55. How does data science help businesses engage with customers?
By showing which engagement strategies are most cost-effective.
56. In the context of data science, what does the term “data deluge” refer to?
The unprecedented amount of data available.
57. According to the text, for whom are data science skills essential?
All professional roles, even those not traditionally quantitative.
58. What is the benefit of integrating data science into traditionally quantitative jobs?
Enhanced understanding of data interactions across various fields.
59. What does the message of “data science for all” emphasize?
Data science affects virtually every aspect of modern life.
60. According to the text, how can an organization’s management use data science for decision-making?
Data science can be a primary tool for addressing top technology opportunities.
61. According to the chapter, what benefits does data science offer to consumers, researchers, and companies?
Data science can enable informed consumption and production choices.
3
© 2026
Chapter Review Questions
Section 1. Introduction to Data Science
1. What is the interdisciplinary field that uses scientific methods to extract knowledge from data?
Data science.
2. What is the primary purpose of data analysis in data science?
To generate insights and inform decision-making.
3. Define a population in the context of data science.
A population is the set of every and all items or individuals that are of interest for a question.
4. What other field, often confused with data science, applies data science principles to domain-specific
problems?
Data analytics.
5. What are some of the modern challenges that have shaped data science?
Increased complexity of data and analysis methods, as well as rapidly increasing amounts of the available
data.
6. What constitutes a sample in data science?
A subset of the population.
7. In the context of data science, what is the significance of understanding samples and populations?
To inform better decision-making in society.
8. What combination of fields does data science encompass?
Statistics, computer science, and domain knowledge.
9. When was the term “data science” first proposed as an alternative for “computer science”?
1974
10. What is the role of a data scientist in the context of the data science lifecycle?
To extract actionable insights from data.
11. What is the most significant reason for the importance of data and their analysis, according to the text?
To describe and draw conclusions about a population based on the data collected for a sample.
Section 2: Data in Tables
12. In data science, what are the entities about which data are recorded called?
xii
© 2026
,Data Science for All
Observational units
13. What are the recorded characteristics of observational units called?
Variables of interest
14. What term is used to refer to the data for a single observational unit?
An observation
15. From the following set of numbers: 0.0, 2.71828, -3, 5, which one is most likely to be stored as a float
variable?
2.71828
16. Which type of variable is used to designate categories?
Categorical
17. In the context of data science, what does it mean for data to be tidy?
When data is tidy, it is structured so that each column represents a distinct variable, each row corresponds
to a unique observation, and each cell holds only one value.
18. What do we call the form when data are represented as a table with rows and columns?
Tabular form
19. Integers and floats are examples of what kind of variables?
Quantitative
20. What kind of variables only take on values of TRUE and FALSE, sharing aspects of both categorical and
quantitative variables?
Boolean
21. What form of quantitative variables consists only of umbers with no decimal points?
Integer
22. Given the numbers 5000, -2.01, 1.3, and 1/2, which one is most likely to be stored as an integer variable?
5000
23. What form of quantitative variables consists of numbers that may have decimal points?
Float
24. Given the set of numbers 0, 2, 5, and 1.5, which is most likely to be converted to a Boolean value in a data
analysis context?
0
25. What form of categorical variables contains words or characters without order or numerical utility?
Text
26. Given the numbers -1.0, 0.0, 3, and 4, which one could be stored as a float or converted to a Boolean
variable?
0.0
27. Given the numbers 2.71828, -5, 3.14, and 2.01, which one is most likely to be stored as an integer in a data
structure?
-5
28. What is metadata?
Data about data.
1
© 2026
, Chapter 1: What Is Data Science?
29. When is data considered messy?
When it is not yet organized in a well-defined structure.
30. Which of the following numbers is likely to be stored as a float variable in a data set: 1, 4.44, 144, or 0?
4.44
31. When data are in tabular form, what is stored in the columns? What is stored in the rows?
The variables are stored in the columns, and the observations are stored in the rows.
32. When the rows and columns of tabular data are fully populated, and each cell of the table corresponds to a
single value, we consider the dataset clean and organized. What is the term we use to describe this?
Tidy
33. Given the numbers 1243424, 1.2, -1.01, and 2.03, which is most likely to be stored as an integer variable?
1243424
34. What is the primary role of a data key or codebook?
To store metadata
35. In a tidy dataset, what does each column represent?
A variable
Section 3: Data Preparation
36. In the context of data science, what does it mean to prepare data?
Accessing, cleaning, transforming, and integrating data.
37. What is the first step in data wrangling?
Clean the data to ensure its quality.
38. For what purpose is data transformation essential?
Organizing and formatting data for efficient analysis.
39. What is the goal of integrating datasets?
To combine two or more sources for richer insights.
40. In the context of data preparation, what does data wrangling refer to?
Converting raw data into formats necessary for analysis.
41. Describe the process of data collection.
Gathering data systematically from various sources.
42. What is the significance of data quality and relevance in data collection?
To form the foundation of analysis and insights.
43. What does the term “unstructured data” mean?
Data that are not organized in predefined formats.
44. What is the process of organizing data (often into tables) called?
Data structuring.
Section 4: Data Analysis and Storytelling
45. What is descriptive analysis in data science is primarily concerned with?
2
© 2026
, Data Science for All
Reporting what has happened.
46. What is the goal of diagnostic analytics in data science?
To explain why something might have happened.
47. What is the goal of predictive analysis in data science?
To estimate future behaviors based on past data.
48. What is the component of data storytelling that allows us to communicate statistics and analysis results in
form of graphs and charts?
Visualization
49. Describe prescriptive analysis in the context of data science.
The recommendation of actions based on predictive analysis.
50. What is the purpose of communication, in the context of data science?
To make information more accessible and guide decision-making.
51. How is data visualization useful in data science?
Answers may vary. Example: Data visualization is useful because it allows the data scientist to show
information concisely in a graphical format, which facilitates storytelling.
52. What is the ultimate goal of data science storytelling?
To effectively communicate analysis results to a wider audience and optimize decision-making across
various fields.
53. What does effective storytelling in data science require?
The ability to replicate results based on the process description.
Section 5: Data Science in Society and Industry
54. What is the primary driver for the growth of data science in society and industry?
B. The increasing amounts of available data.
55. How does data science help businesses engage with customers?
By showing which engagement strategies are most cost-effective.
56. In the context of data science, what does the term “data deluge” refer to?
The unprecedented amount of data available.
57. According to the text, for whom are data science skills essential?
All professional roles, even those not traditionally quantitative.
58. What is the benefit of integrating data science into traditionally quantitative jobs?
Enhanced understanding of data interactions across various fields.
59. What does the message of “data science for all” emphasize?
Data science affects virtually every aspect of modern life.
60. According to the text, how can an organization’s management use data science for decision-making?
Data science can be a primary tool for addressing top technology opportunities.
61. According to the chapter, what benefits does data science offer to consumers, researchers, and companies?
Data science can enable informed consumption and production choices.
3
© 2026
