lesson 3-13-3 cleaning the data

Handling Missing Values and Inconsistent Formatting

Have you ever tried to solve a mystery, but the clues were smudged, ripped, or just didn't make sense? That's exactly what it's like for a Data Scientist! They get huge digital 'clue files' (datasets) to solve big problems, but the clues are often messy. In this lesson, you'll become a Data Detective. You'll learn how to spot the messy clues – the missing information, the typos, and the odd-one-out data points. More importantly, you'll learn the spreadsheet tools to clean it all up, making sure your clues are perfect before you start solving the mystery. This is a vital first step on the Data Science & Analytics pathway!

Learning Outcomes

The Building Blocks (Factual Knowledge)

Recall that real-world data is often "messy".

Describe what is meant by missing values, inconsistent formatting, and outliers in a dataset.

The Connections and Theories (Conceptual Knowledge)

Explain why data cleaning is a crucial step for ensuring accurate analysis.

Analyse how incomplete or biased training data can lead to unfair outcomes in an AI system.

Digital Skill Focus: Sorting, filtering, find and replace using spreadsheets.

The Skills and Methods (Procedural Outcomes)

Apply spreadsheet tools like sorting and filtering to identify inconsistencies and outliers in a dataset.

Use spreadsheet tools like 'Find and Replace' to correct inconsistent data.

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