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Questions and Answers
What is a dataset?
A collection or group of related data that share the same set of attributes or properties.
What are the three main categories of Data Analytics?
Descriptive Analytics aims to contextualize data to generate information.
True
Unstructured Data does not conform to a __________ or schema.
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Match the Data Visualization types with their descriptions:
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What is the main reason why faces are used in data visualization?
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What is the primary consideration when mapping variables to facial features in Chernoff faces?
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What is the primary purpose of a tag cloud?
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What type of data is often visualized using a ThemeRiver?
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What is the primary difference between discrete and continuous data?
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Why are faces effective in data visualization?
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What is the primary advantage of using a tag cloud?
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What is the primary purpose of a Chernoff face?
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What is the primary consideration when creating a ThemeRiver?
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What is the primary limitation of using faces in data visualization?
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Study Notes
Introduction to Data Visualization
- A dataset is a collection of related data that share the same set of attributes or properties.
- Each member of the dataset is called a "datum".
Types of Data
- Human-generated data: comes from human activities, such as surveys, social media, and sensors.
- Machine-generated data: comes from machines, such as sensors, logs, and IoT devices.
Data Organization
- Structured data: follows a specific format and is easily searchable, such as relational databases.
- Semi-structured data: has a defined level of structure, but does not conform to a rigid format, such as XML files.
- Unstructured data: does not conform to a specific format, such as images, audio files, and text files.
Data Analytics
- Refers to the techniques used to analyze data to enhance productivity and business gain.
- Categories:
- Descriptive analytics: answers questions about what happened in the past.
- Diagnostic analytics: answers questions about why something happened.
- Predictive analytics: answers questions about what may happen in the future.
- Prescriptive analytics: answers questions about what should be done.
Data Visualization
- The formation of a mental model of something, not just what a user sees on a computer display.
- Goals:
- Gain insight into an information space by mapping data onto graphical primitives.
- Provide a qualitative overview of large data sets.
- Search for patterns, trends, structure, and relationships among data.
The Power of Visualization
- The ability to visualize data can lead to insights and discoveries.
- Historical example: John Snow's visualization of the cholera epidemic in London in 1854.
Data Visualization Process
- Data → Representation → Presentation → Interaction
- Human performance and cognition are important factors in designing visualization tools.
Representation
- The goal of representation is to present data clearly to the mind.
- Three principles to consider:
- Type: numeric, categorical, ordinal, etc.
- Dimension: the number of attributes can affect visualization.
- User: the characteristics of the user can influence the design and effectiveness of a representation.
Types of Visualization
- Point representation: Tukey's Box Plot.
- Scatterplots: for displaying bivariate data.
- Star plots (Spidergram): for displaying multivariate data.
- Magnification: for showing relative attributes.
- Parallel coordinate plots: for displaying high-dimensional data.
- Iconic representation: using pictorial images to make data easier to understand.
- Chernoff Faces: for displaying multivariate data using a human face.
- Text: for displaying text data, such as tag clouds.
- Time-Varying Data: for showing data that changes over time.
Data Analytics
- Refers to the techniques used to analyze data to enhance productivity and business gain
- Involves extracting data from various sources, cleaning and categorizing it to analyze behavioral patterns
- Techniques and tools used vary according to the organization or individual
Data Analytics Categories
- Descriptive Analytics: contextualizes data to generate information, answers questions about past events
- Diagnostic Analytics: determines the cause of a phenomenon, answers questions about the reason behind an event
- Predictive Analytics: predicts the outcomes of events based on patterns, trends, and exceptions from historical and current data
- Prescriptive Analytics: provides recommendations on what actions to take to achieve a desired outcome
Descriptive Analytics
- Generates information to answer questions about past events
- Examples: What was the sales volume over the past 12 months? What is the monthly commission earned by each sales agent?
Diagnostic Analytics
- Determines the cause of a phenomenon by analyzing data from multiple sources
- Answers questions about the reason behind an event
- Examples: Why were Q2 sales less than Q1 sales? Why was there an increase in patient readmission rates over the past three months?
Predictive Analytics
- Predicts the outcomes of events based on patterns, trends, and exceptions from historical and current data
- Enhances information to generate knowledge that conveys relationships to form basic models for generating predictions
Data Visualization
- Represents data in a way that is easy to understand and interpret
- Involves converting numbers into pictures
Data Visualization Process
- Data: the raw material
- Representation: presenting the data in a clear and meaningful way
- Presentation: communicating the insights and findings effectively
- Interaction: enabling users to explore and navigate the data
Human Performance and Cognition
- Important to consider when designing visualization tools
- Factors to consider: human visual performance, cognition, and interaction
Representation
- Presents data in a clear and meaningful way
- Three principles to identify: type, dimension, and user
- Type: numeric, categorical, ordinal, relationship, text, audio, images, etc.
- Dimension: the more attributes, the more difficult to visualize
- User: the user's characteristics influence the design and effectiveness of a representation
Visualization Techniques
- Point representation: Tukey's Box Plot
- Scatterplots: for bivariate data, identifies global trends, local trade-offs, and outliers
- Star Plots (Spidergram): for displaying multivariate data
- Magnification: shows relative attributes
- Parallel coordinate plots: for multiple parameters
- Iconic representation: uses pictorial images to make actions, objects, and concepts easier to recognize and remember
- Chernoff Faces: displays multivariate data in the shape of a human face
- Text: tag clouds (word clouds) for visualizing keyword metadata
- Time-Varying Data: ThemeRiver for depicting thematic changes in a collection of variables over time
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Description
Learn about the basics of data visualization, including datasets, types of data, and data organization. Understand the concepts of data generation and data types.
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