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The Fundamentals of Data-driven Storytelling
The Fundamentals of Data-driven Storytelling
  • About this course
    • Course Introduction
  • Module 1 - Find
    • 1.1 How to Find Data for Storytelling and journalism
      • Starting with a question
      • Open data portals and platforms
      • Other sources of data
    • 1.2 How to get better data from a Goolge Search
      • Searching for filetypes and formats
      • More on Advanced Search operators
      • Other common Google Search operators
    • 1.3 Sourcing your own data
      • Creating a Google Form for Research
      • Creating a questionnaire with TypeForm
      • Using quizzes and comments as a sources of data
  • Module 2 - Get
    • 2.1 Turning websites and PDFs into machine readable data
      • Scraping data with Tabula
    • 2.2 An introduction to spreadsheet software
      • Google Sheets, Microsoft Excel and Libre Office Calc.
      • Finding your way around a spreadsheet
      • Simple web scraping with Google Sheets
  • Module 3 - Verify
    • 3.1 Can I use this data in my work?
      • Initial steps for verification
      • What do these column headings mean?
  • Module 4 - Clean
    • 4.1 What to do with disorganised data?
      • Why is clean data important?
      • Keep your data organised
      • Cleaning data cheatsheet
  • Module 5 - Analyse
    • 5.1 What is the story within the data?
      • Spreadsheet rows, columns, cells and tabs
        • Spreadsheet formats, forumlas and essential shortcuts
          • Using the VLOOKUP Function
            • Combine Data From Multiple Spreadsheets
    • 5.2 How to turn numbers into stories
  • Module 6 - Visualise
    • 6.1 Ways we visualise data
    • 6.2 Why we visualize Data
    • 6.3 How to visualise data
  • Course Testing & Feedback
    • ⏱️Quick course exam
    • 🎓Extended course exam
    • 📝Survey and feedback
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  1. Module 6 - Visualise

6.2 Why we visualize Data

In this lesson, we will explore the importance of visualising data and the benefits of presenting it in a visual format.

Reasons to visualise data

  • Raw data is hard to make sense of. Using visulisations can help identify patterns, trends, and correlations that may not be apparent in raw data.

  • Using visulisations can help make complex information easier to understand and interpret.

  • Data is visualised to reveal, represent & communicate information more effectively to a wide range of audiences.

By presenting data in visual form we:

Make it easier for stakeholders to grasp complex information

By presenting data in a visual format, stakeholders can quickly and easily understand complex information. Visuals like graphs and charts can help to make the data more accessible and easy to comprehend.

Make it easier for stakeholders to grasp complex information

Visualizing data can help us identify patterns, trends, and correlations that may not be immediately obvious from looking at raw data. By creating visuals like line graphs and scatter plots, we can more easily see how different variables are related to each other.

Identify patterns, trends and correlations

Visualizing data can help us identify patterns, trends, and correlations that may not be immediately obvious from looking at raw data. By creating visuals like line graphs and scatter plots, we can more easily see how different variables are related to each other.

Spot anomalies

By using visuals to represent data, we can more easily spot anomalies or outliers in the data. These anomalies may indicate errors or anomalies that need to be further investigated.

Depict progression, continuity or the reverse thereof:

Visuals can help us understand the progression or continuity of data, or the reverse thereof. For example, a line graph can show how a variable changes over time, or a scatter plot can show whether two variables are positively or negatively correlated.

Define structures

By creating visuals like tree diagrams and flowcharts, we can better understand the structure of our data. These visuals can help us see how different categories or subcategories relate to each other.

Draw comparisons

Visuals can help us draw comparisons between different data points or variables. For example, a bar chart can show how different groups or categories compare to each other in terms of a particular variable.

Radically increase and better manage the variables we are able to comprehend

By presenting data in a visual format, we can more easily manage and comprehend large amounts of data. Visuals can help us quickly and easily see how different variables are related to each other, even if there are many variables to consider.

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Last updated 2 years ago