After completing this topic you will learn about Open Data and test your acquired knowledge on topics such as: a) What is open data and why we need them b) The Value of Open Data in businesses, governments and their impact on society, public policy, culture and the environment and c) How to find, use, clean, organise and visualize Open Data
In this module we will explore the following:
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:
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:
At the end of this module you can try out what you’ve learnt on a spreadsheet.
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:
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:
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.
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: