Introduction to Open Data

In this course  we’ll explore the following 3 topics:
  • What is Open Data;
    • What is open data?
    • What is data?
    • What makes data open?
    • Why do we need open data?
  • Unlocking the Value of Open Data
    • Innovation and growth in businesses
    • Opportunities for governments
    • Impact on society and public policy
    • Benefits for culture and the environment.
  • Finding hidden data on the Web
    • How to locate hidden data
    • What benefits hidden data can provide
    • How to obtain hidden data

Use of Open Data

In this module we will explore the following:

  • How data has changed the way we find stories
  • How data has changed the way we tell stories
  • Examples of great data stories

Understanding your rights to use data

When working with data, it is critical to understand your rights. If data is openly licensed, it can be used immediately. In other circumstances you might have to pay for a licence that grants you use of the data.  There are also copyright exceptions which you can use in some circumstances to ensure the way you are using the data is legal.

This module will cover:

  • Public domain licences
  • Open licence
  • Copyright exceptions
  • Fair use


Organising data

A common challenge in ensuring data quality is difficulties people face when using spreadsheets. When data is properly managed, it is much easier to answer fundamental questions and conduct analysis. Correctly managed data also ensures you aren’t making decisions based on faulty evidence. Knowing how to structure and organise data in a spreadsheet is fundamental to ensuring consistency in your data.

In this module we look at how to effectively structure a spreadsheet for raw data collection. We will cover:

  • Spreadsheet layouts
  • Column titles
  • Header rows and the freeze function
  • Data types

At the end of this module you can try out what you’ve learnt on a spreadsheet.


How to clean your data

Although your data may be adequately managed, often a raw dataset can be riddled with errors. Errors are often not even noticed by data publishers because the data can change over time.
In other cases, errors can be the result of human mistakes in data entry, like typos or incorrect abbreviations.

When working with any data, it is important to know how to find errors and correct them to make the data more useful.

In this module we’ll explore the following:

  • Common data errors
  • Useful data cleaning tools
  • Benefits of cleaning data


Filtering and pivot tables

Once your data is organised and clean, you can now begin to filter and analyse the data.

In this module we will learn how to:

  • Sort and filter data in a spreadsheet
  • Apply formula to generate simple statistics
  • Create a number of pivot tables
  • Create a number of summary graphs

To prepare you for this exercise you will need this spreadsheet. Note that it has two sheets; the first tab holds the data, and the second tab called ‘Statistics’ will be used when we look at formula. 

Data visualisation formats

You may find something interesting in the data when you are cleaning, analysing and interrogating your dataset. If this finding is significant, the next stage will be to communicate this to others. Data visualisations help communicate insight found in data in an easy and quick way.

In this module we are going to cover:

  • A brief history of data visualisation
  • Charts and distributions
  • Visualising data on a map