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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 5 - Analyse

5.1 What is the story within the data?

“I find that most people don’t know what a story is until they sit down to write one.” - Flannery O’Connor

PreviousCleaning data cheatsheetNextSpreadsheet rows, columns, cells and tabs

Last updated 2 years ago

This is the point at which you make sense of the data, and figure out how you can use it to tell your story; in essence, what is the story within the data?

You can use numerous tools for analysis, including , Microsoft Excel, and .

We can begin our process of analysis by:

  • Conducting a data interview.

  • Processing the data using the formulae & functions in order to answer the interview questions, such as filters, pivot tables, etc.

  • Extracting the data insights we have identified.

Before we launch into our data lesson, let’s consider this:

The South African Government has budgeted R208 billion for Economic Development in the current fiscal year (2021/22 Consolidated Budget Summary).

How can we begin to answer the question:

But, Is that a lot?

The South African Government has budgeted R208 billion for Economic Development in the current fiscal year

We can begin our process of uncovering whether it is a lot by looking at the data from different angles:

  1. What are we comparing it to?

  2. What is the amount as a % of the overall budget for the country?

  3. How many people is this budget targeting? And how does that compare to other municipalities?

  4. How does it compare to the national budget in previous years?

  5. What is included in this amount? i.e. buildings, grants, offices, salaries?

In order to look at our data from many angles, we need to be able to process it.

This is the point at which you make sense of the data, and figure out how you can use it to tell your story; in essence, what is the story within the data?

You can use numerous tools for analysis, including Google Sheets, Microsoft Excel, and Tableau Public.

Data stories are often linked to the types of technical processes you are able to perform on the data in order to extract the relevant data insights. They speak to stories about comparison, trends, scale, transformation, proportion, range, distribution, and more.

We can begin our process of analysis by:

  • Conducting a data interview.

  • Processing the data using the formulae & functions in order to answer the interview questions, such as filters, pivot tables, etc.

  • Extracting the data insights we have identified.

Google Sheets
Tableau Public