Data Science for All, 1e (Glanz/Davis)
Chapter 1 What is Data Science?
1.1 Introduction to Data Science
1 Define terms relating to data science.
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
knowledge from data.
B) Data science is solely focused on statistical analysis.
C) Data science is primarily about data visualization.
D) Data science only involves machine learning.
Answer: A
2) What are the three iterative components of the data science life cycle?
A) Data preparation, analysis, and storytelling
B) Data collection, visualization, and deletion
C) Data extraction, transformation, and archiving
D) Data acquisition, modeling, and disposal
Answer: A
2 Identify the scope of data science including its interdisciplinary nature or life cycle.
1) Which term is defined as the process of collecting, cleaning, transforming, integrating, and
managing data for effective analysis and storytelling?
A) Data preparation
B) Data visualization
C) Data modeling
D) Data archiving
Answer: A
2) In the context of data science, what does "data storytelling" refer to?
A) Communicating data insights through summaries, visualizations, and narratives.
B) Collecting and cleaning raw data.
C) Analyzing data to find patterns.
D) Storing data in a structured format.
Answer: A
1
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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.
C) It is limited to computer science applications.
D) It only involves data visualization.
Answer: A
2) What process involves cleaning, transforming, and integrating data to make them useful for
analysis and storytelling?
A) Data wrangling
B) Data visualization
C) Data modeling
D) Data archiving
Answer: A
1.2 Data in Tables
1 Answer questions about the relationship of observations and variables.
1) What is a Boolean variable type in data tables?
A) A variable that contains only two possible values, TRUE or FALSE
B) A variable that takes on only numerical values
C) A variable that can take on any text
D) A variable that describes a category
Answer: A
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
characteristic of that entity.
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.
D) A variable is always a numerical value associated with an observational unit.
Answer: A
2 Answer questions about tidying messy data.
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
2
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,2) Which of the following scenarios best illustrates tidy data?
A) A table where each column is a variable, each row is an observation, and each cell contains a
single value
B) A table where each column is an observation, each row is a variable, and each cell contains
multiple values
C) A table where each cell contains a summary of multiple observations
D) A table where each row is a dataset, and each column is a variable
Answer: A
3 Identify different variables value and data types.
1) Looking at the numbers -3, 1.4, 1.7, and 1.9, which is most likely to be stored as an integer in
a data structure?
A) -3
B) 1.4
C) 1.7
D) 1.9
Answer: A
2) Consider the values -2, 1, 3, and 4.0. Which of these could be stored as an integer or converted
to a Boolean variable?
A) 1
B) -2
C) 3
D) 4.0
Answer: A
4 Describe metadata and its purpose.
1) Which of the following is NOT a characteristic that would be included in metadata?
A) Description of the analysis process
B) Variable names
C) Data set authorship
D) The coding scheme used for variables
Answer: A
2) Often, data are accompanied by a description of the data itself. Because it is data about data,
what is this data description called?
A) metadata
B) keydata
C) index data
D) exclusive data
Answer: A
3
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, 1.3 Data Preparation
1 Define or describe the data wrangling process.
1) Which is not a part of the data preparation process?
A) Data storytelling
B) Data collection
C) Data wrangling
D) Data management
Answer: A
2) Which of the following activities is NOT part of the data wrangling process?
A) Developing machine learning models
B) Cleaning incomplete or inconsistent data
C) Transforming data into a structured format
D) Integrating multiple data sources
Answer: A
2 Identify and use the steps for data wrangling.
1) Which of the following is an example of raw data?
A) Data in the form originally collected
B) A processed spreadsheet ready for analysis
C) Data that has been cleaned and structured
D) A visual representation of analyzed data
Answer: A
2) What step in the data wrangling process involves merging multiple data sources?
A) Integrating data
B) Cleaning data
C) Transforming data
D) Visualizing data
Answer: A
1.4 Data Analysis and Storytelling
1 Describe or define the different types of data analysis.
1) Good data communication is necessary for what purpose?
A) Making information accessible to a broader audience
B) The transformation of raw data
C) The integration of different data sets
D) Collecting high-quality data
Answer: A
4
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Chapter 1 What is Data Science?
1.1 Introduction to Data Science
1 Define terms relating to data science.
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
knowledge from data.
B) Data science is solely focused on statistical analysis.
C) Data science is primarily about data visualization.
D) Data science only involves machine learning.
Answer: A
2) What are the three iterative components of the data science life cycle?
A) Data preparation, analysis, and storytelling
B) Data collection, visualization, and deletion
C) Data extraction, transformation, and archiving
D) Data acquisition, modeling, and disposal
Answer: A
2 Identify the scope of data science including its interdisciplinary nature or life cycle.
1) Which term is defined as the process of collecting, cleaning, transforming, integrating, and
managing data for effective analysis and storytelling?
A) Data preparation
B) Data visualization
C) Data modeling
D) Data archiving
Answer: A
2) In the context of data science, what does "data storytelling" refer to?
A) Communicating data insights through summaries, visualizations, and narratives.
B) Collecting and cleaning raw data.
C) Analyzing data to find patterns.
D) Storing data in a structured format.
Answer: A
1
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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.
C) It is limited to computer science applications.
D) It only involves data visualization.
Answer: A
2) What process involves cleaning, transforming, and integrating data to make them useful for
analysis and storytelling?
A) Data wrangling
B) Data visualization
C) Data modeling
D) Data archiving
Answer: A
1.2 Data in Tables
1 Answer questions about the relationship of observations and variables.
1) What is a Boolean variable type in data tables?
A) A variable that contains only two possible values, TRUE or FALSE
B) A variable that takes on only numerical values
C) A variable that can take on any text
D) A variable that describes a category
Answer: A
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
characteristic of that entity.
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.
D) A variable is always a numerical value associated with an observational unit.
Answer: A
2 Answer questions about tidying messy data.
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
2
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,2) Which of the following scenarios best illustrates tidy data?
A) A table where each column is a variable, each row is an observation, and each cell contains a
single value
B) A table where each column is an observation, each row is a variable, and each cell contains
multiple values
C) A table where each cell contains a summary of multiple observations
D) A table where each row is a dataset, and each column is a variable
Answer: A
3 Identify different variables value and data types.
1) Looking at the numbers -3, 1.4, 1.7, and 1.9, which is most likely to be stored as an integer in
a data structure?
A) -3
B) 1.4
C) 1.7
D) 1.9
Answer: A
2) Consider the values -2, 1, 3, and 4.0. Which of these could be stored as an integer or converted
to a Boolean variable?
A) 1
B) -2
C) 3
D) 4.0
Answer: A
4 Describe metadata and its purpose.
1) Which of the following is NOT a characteristic that would be included in metadata?
A) Description of the analysis process
B) Variable names
C) Data set authorship
D) The coding scheme used for variables
Answer: A
2) Often, data are accompanied by a description of the data itself. Because it is data about data,
what is this data description called?
A) metadata
B) keydata
C) index data
D) exclusive data
Answer: A
3
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, 1.3 Data Preparation
1 Define or describe the data wrangling process.
1) Which is not a part of the data preparation process?
A) Data storytelling
B) Data collection
C) Data wrangling
D) Data management
Answer: A
2) Which of the following activities is NOT part of the data wrangling process?
A) Developing machine learning models
B) Cleaning incomplete or inconsistent data
C) Transforming data into a structured format
D) Integrating multiple data sources
Answer: A
2 Identify and use the steps for data wrangling.
1) Which of the following is an example of raw data?
A) Data in the form originally collected
B) A processed spreadsheet ready for analysis
C) Data that has been cleaned and structured
D) A visual representation of analyzed data
Answer: A
2) What step in the data wrangling process involves merging multiple data sources?
A) Integrating data
B) Cleaning data
C) Transforming data
D) Visualizing data
Answer: A
1.4 Data Analysis and Storytelling
1 Describe or define the different types of data analysis.
1) Good data communication is necessary for what purpose?
A) Making information accessible to a broader audience
B) The transformation of raw data
C) The integration of different data sets
D) Collecting high-quality data
Answer: A
4
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