Introduction

All programs work with some sort of data. Some work with large data sets and derive information from it—for example, predicting the weather based on meteorological instruments around the globe. But data is often collected in a way that can introduce errors. Scientific instruments can fail, sensors can be miscalibrated, and people can reply incorrectly in surveys. If you work with data that might contain errors, you'll need to find strategies to deal with them.

In this lesson, you'll work with a sample data set that contains errors. You'll use multiple methods to clean the data, enabling you to report the most accurate information possible.

Go Build

Open the Processing Data.playground file in your course resources and follow the instructions.

Screenshot of the Processing Data playground

Reflection Questions

What kinds of errors might arise when collecting data from people?

How can a data collection process be designed to minimize errors?

Can you think of ways to measure the accuracy of a data set?

Can you think of errors in data sets that would be difficult or impossible to correct? Is it possible to have a data set that's so corrupted by errors that it's unusable?

Summary

You've learned how you can assess the accuracy of a data set and correct for errors. You've also used and cited third-party code in your algorithms. In the next lesson, you'll apply all your knowledge to create pixel art.