p.2
3C's of Data Science: Computation, Comprehension, Communication
What are the 3C's of Data Science?
Computation, Comprehension, Communication.
p.48
Principles of Effectiveness and Expressiveness in Data Visualization
What does the expressiveness principle entail?
Match channel and data characteristics.
p.12
Role of Visualization in Enhancing Human Capabilities
When is visualization suitable to use?
When there is a need to augment human capabilities rather than replace people with computational decision-making methods.
p.16
Types of Data: Multivariate, Time-Varying, Hierarchical
What does the count '0' represent in the data?
It likely indicates a count of a specific category or measurement.
p.10
Visualization Techniques and Misleading Graphs
How can visualization mislead?
It can mislead intentionally or unintentionally.
p.48
Principles of Effectiveness and Expressiveness in Data Visualization
Why is it important to match channel and data characteristics?
To enhance the expressiveness of the visualization.
p.12
Data Visualization Fundamentals
What question should be considered regarding data presentation?
Can we do better than a data table?
p.48
Principles of Effectiveness and Expressiveness in Data Visualization
What is the effectiveness principle in data visualization?
Encode the most important attributes with the highest ranked channels.
p.35
Marks and Channels in Visualization
What is the second mark and channel combination for encoding visually?
Vertical position and horizontal position with a point mark.
p.46
Visualization Techniques and Misleading Graphs
Why is sorting important in a bar chart?
Sorting helps to easily identify trends, comparisons, and outliers in the data.
p.7
3C's of Data Science: Computation, Comprehension, Communication
What is the single biggest problem in communication according to George Bernard Shaw?
The illusion that it has taken place.
p.40
Principles of Effectiveness and Expressiveness in Data Visualization
What should not be shown in data visualization according to the principle of expressiveness?
What is not implied by the data.
p.46
Causal Reasoning in Data Interpretation
How does sorting affect data interpretation in bar charts?
Sorting can enhance clarity and make it easier for viewers to understand the data at a glance.
p.22
Types of Data: Multivariate, Time-Varying, Hierarchical
What is quantitative data?
Data that can be measured and expressed numerically.
p.24
Role of Visualization in Enhancing Human Capabilities
What is the purpose of visual representations in computer-based visualization systems?
To help people carry out tasks more effectively.
p.37
Principles of Effectiveness and Expressiveness in Data Visualization
What are visual variables?
Elements like color, size, shape, and position used to represent data visually.
p.11
Causal Reasoning in Data Interpretation
What is the role of predictive power in data?
It helps in making informed decisions.
p.2
3C's of Data Science: Computation, Comprehension, Communication
What role does 'Communication' play in Data Science?
It involves conveying data findings effectively to stakeholders.
p.22
Types of Data: Multivariate, Time-Varying, Hierarchical
What are interval attributes?
Attributes that have meaningful intervals between values but no true zero.
p.6
3C's of Data Science: Computation, Comprehension, Communication
Who are the authors of the paper discussing generating datasets with varied appearance?
Matejka, Justin, and George Fitzmaurice.
p.15
Data Visualization Fundamentals
What do computer-based visualization systems provide?
Visual representations of datasets.
p.3
Human-Centered Data Science
What is the goal of developing novel methods in human-centered data science?
To help experts access and trust relevant information and understand its context for making analytical judgments.
p.24
Role of Visualization in Enhancing Human Capabilities
What do computer-based visualization systems provide?
Visual representations of datasets.
p.13
Data Visualization Fundamentals
What is the main focus of visualization according to Munzner?
To aid in understanding and performing tasks with datasets.
p.32
Marks and Channels in Visualization
What is the purpose of visual representations in computer-based visualization systems?
To help people carry out tasks more effectively.
p.22
Types of Data: Multivariate, Time-Varying, Hierarchical
What is qualitative data?
Data that describes categories or qualities.
p.2
3C's of Data Science: Computation, Comprehension, Communication
What is meant by 'Comprehension' in Data Science?
The ability to understand and interpret data insights.
p.23
Types of Data: Multivariate, Time-Varying, Hierarchical
What distinguishes nominal attributes from ordinal attributes?
Nominal attributes have no inherent order, while ordinal attributes have a defined order.
p.2
3C's of Data Science: Computation, Comprehension, Communication
What does the term 'aha!' signify in Data Science?
A moment of realization or insight gained from data analysis.
p.6
Human-Machine Collaboration
What is the main argument presented by Francis J. Anscombe regarding computers?
A computer should make both calculations and graphs, as both contribute to understanding.
p.50
Marks and Channels in Visualization
Can using too many channels in a visualization be misleading?
Yes, it can lead to confusion and misinterpretation of the data.
p.15
Data Visualization Fundamentals
What is the purpose of visual representations in computer-based visualization systems?
To help people carry out tasks more effectively.
p.13
Data Visualization Fundamentals
What do computer-based visualization systems provide?
Visual representations of datasets.
p.14
Visualization Techniques and Misleading Graphs
What is Anscombe’s Quartet?
A set of four datasets that illustrate the importance of graphing data to understand statistical properties.
p.14
Human-Centered Data Science
How do visualizations aid cognition?
They provide a perceptual proxy for higher-level cognition.
p.37
Principles of Effectiveness and Expressiveness in Data Visualization
Why is it important to match data attributes with visual variables?
To enhance the clarity and effectiveness of the data presentation.
p.22
Types of Data: Multivariate, Time-Varying, Hierarchical
What are ratio attributes?
Attributes that have meaningful intervals and a true zero point.
p.6
Causal Reasoning in Data Interpretation
What is the title of the paper by Francis J. Anscombe?
Graphs in Statistical Analysis.
p.30
3C's of Data Science: Computation, Comprehension, Communication
How does non-verbal communication impact interactions?
It conveys emotions and attitudes, often more powerfully than words.
p.4
Causal Reasoning in Data Interpretation
What is the importance of causal reasoning in data interpretation?
It helps determine if the evidence supports the facts.
p.4
Human-Machine Collaboration
What is the role of a judge in the context of AI?
To evaluate the outcomes and effectiveness of AI recommendations.
p.35
Marks and Channels in Visualization
What is the first mark and channel combination for encoding visually?
Vertical position with a bar mark.
p.46
Visualization Techniques and Misleading Graphs
What is a sorted bar chart?
A bar chart where the bars are arranged in a specific order, typically based on the values they represent.
p.11
3C's of Data Science: Computation, Comprehension, Communication
What is data?
A set of observations or outcomes of real-world phenomena.
p.2
3C's of Data Science: Computation, Comprehension, Communication
What does 'Computation' refer to in the context of Data Science?
The process of using algorithms and models to analyze data.
p.23
Types of Data: Multivariate, Time-Varying, Hierarchical
What is an example of a quantitative attribute?
Height, weight, or temperature.
p.2
3C's of Data Science: Computation, Comprehension, Communication
What is 'Inference' in the context of Data Science?
Drawing conclusions from data analysis.
p.6
Visualization Techniques and Misleading Graphs
What concept does the phrase 'same summary statistic, different visual structures' illustrate?
The idea that different visual representations can convey the same statistical information.
p.30
3C's of Data Science: Computation, Comprehension, Communication
What is the primary purpose of communication?
To share information and ideas between individuals or groups.
p.50
Marks and Channels in Visualization
How can size be used as a channel in data visualization?
By varying the size of data points to indicate magnitude or importance.
p.4
Human-Centered Data Science
What role does a teacher play in the context of AI and human interaction?
A teacher helps align AI systems with human mental models.
p.29
Causal Reasoning in Data Interpretation
What types of relationships can be detected in data analysis?
Similarity, magnitude difference, correlation, etc.
p.40
Principles of Effectiveness and Expressiveness in Data Visualization
What does expressiveness in data visualization emphasize?
It emphasizes what is intended (task).
p.12
Human-Centered Data Science
What is an important consideration regarding the role of humans in data tasks?
What is the role of the human?
p.37
Principles of Effectiveness and Expressiveness in Data Visualization
What is the relationship between data attributes and visual variables?
Important data attributes should be matched with highly ranked visual variables for effective visualization.
p.32
Marks and Channels in Visualization
What do computer-based visualization systems provide?
Visual representations of datasets.
p.37
Principles of Effectiveness and Expressiveness in Data Visualization
What can happen if data attributes are not matched with appropriate visual variables?
It can lead to misinterpretation or confusion in understanding the data.
p.22
Types of Data: Multivariate, Time-Varying, Hierarchical
What are nominal attributes?
Attributes that represent categories without a specific order.
p.11
Causal Reasoning in Data Interpretation
What does 'aha!' signify in data analysis?
A moment of realization or discovery of patterns.
p.47
Marks and Channels in Visualization
Why is ranking of channels important in data visualization?
Ranking helps determine which visual properties are most effective for conveying specific types of data.
p.30
3C's of Data Science: Computation, Comprehension, Communication
What role does active listening play in communication?
It ensures understanding and fosters a positive exchange of ideas.
p.4
Human-Machine Collaboration
What is the purpose of recommending datasets or labels?
To facilitate more effective exploration and querying.
p.21
Types of Data: Multivariate, Time-Varying, Hierarchical
What type of data will be primarily dealt with?
Tabular, multivariate data.
p.33
Marks and Channels in Visualization
What are channels in data visualization?
They control the appearance of marks.
p.14
Role of Visualization in Enhancing Human Capabilities
What is the purpose of computer-based visualization systems?
To provide visual representations of datasets that help people carry out tasks more effectively.
p.14
3C's of Data Science: Computation, Comprehension, Communication
What did Francis J. Anscombe emphasize about computer output?
That both calculations and graphs should be studied as each contributes to understanding.
p.16
Types of Data: Multivariate, Time-Varying, Hierarchical
What does the count '2' represent in the data?
It likely indicates a count of a specific category or measurement.
p.23
Types of Data: Multivariate, Time-Varying, Hierarchical
What are the main types of attribute data in data analysis?
Qualitative and quantitative attributes.
p.23
Types of Data: Multivariate, Time-Varying, Hierarchical
What are continuous and discrete attributes?
Continuous attributes can take any value within a range, while discrete attributes can only take specific values.
p.11
Causal Reasoning in Data Interpretation
What is a model in the context of data?
A representation used to understand and predict outcomes.
p.30
3C's of Data Science: Computation, Comprehension, Communication
Why is clarity important in communication?
It helps prevent misunderstandings and ensures the message is received as intended.
p.48
Principles of Effectiveness and Expressiveness in Data Visualization
How should channels be ranked according to the effectiveness principle?
Channels should be ranked based on their ability to encode important attributes.
p.13
Data Visualization Fundamentals
What is the purpose of visual representations in computer-based visualization systems?
To help people carry out tasks more effectively.
p.46
Visualization Techniques and Misleading Graphs
What are the common sorting methods for bar charts?
Common methods include sorting by value (ascending or descending) or by category.
p.23
Types of Data: Multivariate, Time-Varying, Hierarchical
What is a qualitative attribute?
An attribute that describes a characteristic or quality, often categorical.
p.23
Types of Data: Multivariate, Time-Varying, Hierarchical
What is a quantitative attribute?
An attribute that represents numerical values and can be measured.
p.25
Human-Centered Data Science
What year is associated with the data provided?
1960, 1970, 1980, 1990, 2000, 2010.
p.22
Types of Data: Multivariate, Time-Varying, Hierarchical
What are ordinal attributes?
Attributes that represent categories with a meaningful order.
p.50
Marks and Channels in Visualization
What is an example of using color as a channel in data visualization?
Using different colors to represent different categories in a bar chart.
p.4
Human-Machine Collaboration
What is cold-start exploration in the context of data querying?
It refers to the challenge of making recommendations or decisions with limited prior data.
p.3
3C's of Data Science: Computation, Comprehension, Communication
What three key components are emphasized in the context of data science?
Comprehension, Computation, and Communication.
p.12
3C's of Data Science: Computation, Comprehension, Communication
What should be evaluated about tasks in relation to visualization?
Can the task be automated?
p.28
3C's of Data Science: Computation, Comprehension, Communication
What is the purpose of comparing profit across different years?
To analyze financial performance and growth trends over time.
p.28
3C's of Data Science: Computation, Comprehension, Communication
What can a year-on-year profit comparison reveal?
It can indicate whether a business is growing, stable, or declining.
p.28
Causal Reasoning in Data Interpretation
What factors might affect profit from year to year?
Market conditions, operational efficiency, and changes in consumer demand.
p.18
Types of Data: Multivariate, Time-Varying, Hierarchical
What is the relationship between Bob and Mary?
They are parent and child.
p.11
Human-Machine Collaboration
What is the significance of machine learning in data?
It enables the discovery of patterns and inference.
p.47
Marks and Channels in Visualization
What are channels in data visualization?
Channels are the visual properties used to represent data, such as color, size, shape, and position.
p.30
3C's of Data Science: Computation, Comprehension, Communication
What are the key components of effective communication?
Sender, message, medium, receiver, and feedback.
p.50
Marks and Channels in Visualization
What is the benefit of combining multiple channels in a single visualization?
It allows for a richer and more nuanced representation of data.
p.11
3C's of Data Science: Computation, Comprehension, Communication
What are key factors that influence data usability?
Scale, value, availability, and accessibility.
p.20
Types of Data: Multivariate, Time-Varying, Hierarchical
Who is older, Bob or Jane?
Bob (47 years) is older than Jane (18 years).
p.11
Human-Centered Data Science
What is human-data interaction?
The way humans engage with and interpret data.
p.4
Human-Machine Collaboration
How can humans help train machine learning models?
By providing direct (labels) or indirect (semantic interaction) feedback.
p.46
Visualization Techniques and Misleading Graphs
What is a potential drawback of sorted bar charts?
They may oversimplify data or mislead viewers if important context is omitted.
p.22
Types of Data: Multivariate, Time-Varying, Hierarchical
What are the main types of attribute data in data analysis?
Qualitative and Quantitative.
p.11
Data Visualization Fundamentals
What processes are involved in handling data?
Processing, storage, and visualization.
p.50
Marks and Channels in Visualization
What are multiple channels in data visualization?
Different methods or dimensions used to represent data, such as color, size, shape, and position.
p.23
Types of Data: Multivariate, Time-Varying, Hierarchical
What is an example of a qualitative attribute?
Color, gender, or type of product.
p.20
Types of Data: Multivariate, Time-Varying, Hierarchical
Who is younger, Joe or Mary?
Joe (21 years) is younger than Mary (49 years).
p.47
Marks and Channels in Visualization
What is typically considered the most effective channel for quantitative data?
Position along a common scale.
p.50
Marks and Channels in Visualization
Why are multiple channels used in data visualization?
To convey more information and enhance understanding of complex data.
p.50
Marks and Channels in Visualization
How does position serve as a channel in data visualization?
By placing data points at different locations on a graph to indicate their values.
p.4
Human-Centered Data Science
How can interactive semantic reasoning benefit data exploration?
It allows for more nuanced querying and hypothesis generation.
p.11
Causal Reasoning in Data Interpretation
What are patterns in data?
Recurring themes or trends that can be identified through analysis.
p.47
Marks and Channels in Visualization
How does the ranking of channels affect data interpretation?
It influences how easily and accurately viewers can understand the data presented.
p.4
Human-Machine Collaboration
What is the role of AI in human-machine collaboration?
AI assists in enhancing human capabilities and decision-making.
p.6
Visualization Techniques and Misleading Graphs
What does the Datasaurus project demonstrate?
How datasets can have the same statistics but different visual appearances.
p.50
Marks and Channels in Visualization
What is an example of using shape as a channel in data visualization?
Using different shapes to represent different types of data points in a scatter plot.
p.4
Causal Reasoning in Data Interpretation
What does post-hoc model interpretation involve?
Analyzing the model's decisions after they have been made.
