Topic Five: Visualising the Evidence: From Creating Charts to Choosing the Right Visualisation (in-Class Tutorial Notes)

Overview of Workshop

  • Purpose: Introduction to evidence-based data visualization.

  • Agenda: Discuss data visualization for 14 minutes, then assist with exercises due Friday.

  • Group formation: Planning for group projects presentable at semester's end.

Data Visualization Discussion

Data Visualization Basics

  • Importance of selecting appropriate chart types to effectively communicate findings.

  • Central question for visualization: What do you want to convey?

Exercises Overview

Exercise One

  • Dataset: Provided from previous weeks (Week 3).

  • Requirements: Calculate:

    • Mean

    • Median

    • Variance

    • Standard Deviation

  • Graph Requirement: Create a chart of results.

Exercise Two

  • Dataset: Provided from previous weeks (Week 4).

  • Requirements: Calculate:

    • Mean

    • Median

    • Variance

    • Standard Deviation

    • Pearson Correlation

  • Graph Requirement: Create a scatter plot with a trend line.

  • Interpretation: Must interpret results based on visualizations created.

Creating Visualizations

Types of Charts

  1. Bar Charts

    • Use: Compare categories.

    • Example: Comparing sales across different stores in various demographic areas.

  2. Clustered Column Charts

    • Use: Group comparisons, displaying multiple variables within each category.

    • Example: Sales data from three different locations.

  3. Scatter Plots

    • Use: Show relationships between two continuous variables.

    • Applied in Exercise Two.

  4. Line Charts

    • Use: Show data trends over time.

    • Ideal for spotting growth or decline.

Key Visualization Purposes

  • Complexity Reduction: Summarizes key features of data.

  • Comparison Facilitation: Enables easier visual comparisons between categories rather than using raw numbers.

  • Relationship Detection: Identifies relationships between variables.

Conditional Formatting in Excel

  • Enhances data visualization by automatically changing the appearance of cells based on the values, highlighting increases/decreases visually.

  • Example: Sales figures displayed as data bars and icons illustrating performance trends.

Visualization Techniques for Data Interpretation

  • Choosing the correct type of visualization is critical to conveying the right message.

  • For comparing different variables:

    • Use Bar Charts or Clustered Columns.

  • For assessing relationships:

    • Use Scatter Plots.

  • For examining trends:

    • Use Line Charts or Quadratic equations for data shape assessment.

Dashboard Design Principles

  • Definition: A dashboard is a curated collection of visualizations aimed at answering specific questions.

  • Characteristics of a Good Dashboard:

    • Centers around a relevant query.

    • Each visualization provides insights pertinent to the question.

    • Must tell a cohesive story rather than just display data.

Group Project Details

  • Form groups of 3-5 members.

  • Develop a research question and determine the type of data to collect based on interest.

  • Potential Topics Include:

    • Petrol price trends and effects on consumer behavior.

    • Sales trends of various product types across demographic areas.

    • Analysis of various goods as normal vs inferior goods based on income changes.

  • Final output: Poster or infographic showcasing findings through chosen visualizations.

Statistics Foundations

  • Statistics is essential in transforming raw data into insights by summarizing vast datasets clearly.

  • Purpose:

    • Ensure that visualizations represent true patterns, trends, and relationships.

    • Provide rigor to prevent misleading interpretations, forming a basis for sound decision-making.

    • Example of successful statistical implementation would be interpreting the impact of government changes on fuel prices.

Key Economic Concepts

  1. Normal Goods:

    • Definition: Goods for which demand increases as income rises.

    • ESsentially luxurious goods may be the first cut during crises.

  2. Inferior Goods:

    • Definition: Goods for which demand decreases as income rises.

    • Example: Fast food (e.g., McDonald's) can be viewed as inferior in times of increased consumer income, leading to decreased consumption.

Summary of Good Practices

  • Visualize societal or economic trends using dashboards to engage stakeholders.

  • Connect theoretical concepts (e.g., luxury vs. normal goods) with visual analytics to assess consumer behavior.

  • Identify community needs through data collection to inform relevant queries for a compelling final project.

Project Implementation Steps

  1. Formulate groups.

  2. Select a topic that interests all members.

  3. Collect relevant datasets.

  4. Create comprehensive visualizations that interpret the gathered data clearly.

  5. Prepare presentations for final submission, focusing on clarity and insight.

Conclusion

  • Networking and collaboration are stressed. Use this workshop to engage with peers for maximum understanding and to develop your project effectively.

  • Prepare for future classes on how to present data effectively and analyze market needs through visualization.