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Year 9 - Computing

Data cleansing

Using data science

Unit Summary

In this unit pupils will discover how to use data to investigate problems and make changes to the world around them. Pupils will be exposed to both global and local data sets and gain an understanding of how visualising data can help with the process of identifying patterns and trends.

Lesson Summary

You will learn to describe the need for data cleansing and apply data cleansing techniques to a data set.

Key Notes

  • Data cleansing involves detecting and correcting, or removing, corrupt or inaccurate data.
  • Data cleansing is important because real-world data is often messy, with errors or missing information.
  • Once the data is clean, charts or graphs can be created to help understand patterns and trends.

Vocabulary To Learn

  • data cleansing: the process of detecting and correcting, or removing, corrupt or inaccurate data from a data set

Common Mistakes To Avoid

  • If you have missing data and cannot find the original source, there is nothing you can do.

3 Quick Questions (With Answers)

1. Describe the system or process from this lesson in clear steps.

Data cleansing involves detecting and correcting, or removing, corrupt or inaccurate data. Data cleansing is important because real-world data is often messy, with errors or missing information.

2. Define this computing term and give one practical example. 'data cleansing'

the process of detecting and correcting, or removing, corrupt or inaccurate data from a data set. Add one real device or system example to prove understanding.

3. Correct this common computing misconception.

Mistake: If you have missing data and cannot find the original source, there is nothing you can do. Correction: You could look at similar data and generate an average value to insert. For example, if the zoo is missing a weight for a lion, they could calculate the average weight of the other lions and enter this.

More Lessons In This Unit

Browse all guides in the Year 9 Computing guide library.