Analyzing Data Visualization Requirements

https://www.pluralsight.com/courses/analyzing-data-visualization-requirements

by Chris Achard


Mod 1: Course Overview

  1. Course Overview
Mod 2: Visualizing Quantities or Qualities

  1. Introducing Qualitative and Quantitative Visualizations
  2. Defining Qualitative and Quantitative Visualizations
    • Quantitative
      • Visualization that shows numerical values
      • scatter plot and line graph
    • Qualitative
      • Visualization that shows information that is not directly counted or measured
      • word cloud
  3. Exploring Examples of Quantitative Visualizations
    • Charts and graphs are some of the most basic and most common types of quantitative data visualizations.
    • even if a visualization doesn't even actually contain numbers on it directly, it can still be considered quantitative (for example pie chart)
  4. Exploring Examples of Qualitative Visualizations
    • a flow diagram is a type of qualitative diagram
    • common Qualitative Visualization attributes are: 
      • Color
      • Size
      • Position
      • Orientation
      • Connections
  5. Evaluating Strengths and Weaknesses of Qualitative and Quantitative Visualizations
    • same data can be used to create a quantitative and a qualitative visualization
    • Qualitative Visualizations pros
      • Capture a lot of information
      • Overview
      • Ability to show non-quantitative information
    • Qualitative Visualizations cons
      • Not as precise
      • More difficult to create
    • Quantitative Visualizations pros
      • Specific and precise
      • Research and reports
      • Trends, and changes over time
      • Easier to create
    • Quantitative Visualizations cons
      • Difficult to read and interpret
      • Less interesting
  6. Combining Quantitative and Qualitative Visualizations
  7. Demo: Choosing a Qualitative or Quantitative Visualization for a Dataset
Mod 3: Visualizing Numerical Data and Categories
  1. Introducing Numerical and Categorical Data
  2. Defining Numerical and Categorical Data
    • quantitative data can be classified as Numerical or Categorical
      • Numerical: values that can be added up or sorted
      • Categorical: numerical values that do not have mathematical meaning (for example zip code)
  3. Splitting Numerical Data into Discrete and Continuous
  4. Splitting Categorical Data into Nominal and Ordinal
  5. Determining Whether Data is Numerical or Categorical
  6. Converting Between Numerical and Categorical Data
  7. Choosing Visualizations for Numerical Data
  8. Choosing Visualizations for Categorical Data
  9. Demo: Dividing Data Into Numerical and Categorical
Mod 4: Visualizing to Explain or Explore
  1. Introduction to Exploratory and Expository Visualizations
  2. Defining Exploratory and Expository Visualizations
  3. c 03
  4. Examples of Expository Visualizations
  5. How Not to Exaggerate with Expository Data Visualizations
  6. Demo: Exploratory and Expository Visualizations
Mod 5: Selecting Data to Explain or Explore
  1. Introduction to Data Selection
  2. Why Data Selection Matters
  3. Case Study: Dashboard vs Report
  4. Causation vs Correlation: Be Careful with Expository Visualizations
  5. When to Throw Data Away
  6. Demo: Selecting Data for Visualizations
Mod 6: Displaying the Appropriate Level of Detail
  1. Introduction to Data Detail Levels
  2. Why Choosing a Level of Detail Matters
  3. Showing Trends vs. Detailed Reports
  4. Examples of Different Detail Levels in Visualizations
  5. Considering the Visualization Medium
  6. Changing Scales: When to Adjust the Y-Axis
  7. Demo: Choosing an Appropriate Level of Detail

Mod 7: Providing an Appropriate Visualization for Your Audience
  1. Introduction to Appropriate Visualizations for your Audience
  2. Understanding Viewer Context for Visualizations
  3. Understanding Visualization Context
  4. Medium Matters: Resolution, Color and Surrounding Text
  5. Demo: Adjusting Visualizations for an Audience
Mod 8:: Applying Data Visualization Requirement Analysis
  1. Introduction to Applying Data Visualization Requirement Analysis
  2. Classifying Data Types
  3. Creating Exploratory Data Visualizations
  4. Selecting Data for an Expository Visualization
  5. Creating an Expository Visualization
  6. Adapting an Expository Visualization for Different Audiences
  7. Reviewing the Data Visualization Process

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