Year 10 - Computing
Data driven models
Data science: AI and machine learning
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This pupil-friendly study guide includes a unit summary, clear notes, common mistakes, and quick questions with answers.
Unit Summary
This unit introduces pupils to the emerging field of data science. They will explore what AI systems are and the different categories of AI. The concept of machine learning and how models are driven by data will be explored. They will also consider the ethical and wider implications of AI systems.
Lesson Summary
You will learn to recognise that AI systems rely on data-driven models and the importance of data quality.
Key Notes
- Data-driven models find patterns in data to make decisions or predictions.
- Accurate, complete and unbiased data is essential for effective AI.
- Poor data leads to wrong predictions and unreliable AI results.
Vocabulary To Learn
- bias: when something is unfair towards or against something or someone
- cleaning: dealing with various issues that are commonly found in raw data sets, such as missing data, duplicated records and outliers
Common Mistakes To Avoid
- A model can be improved by providing more training data.
3 Quick Questions (With Answers)
1. Describe the system or process from this lesson in clear steps.
Data-driven models find patterns in data to make decisions or predictions. Accurate, complete and unbiased data is essential for effective AI.
2. Define this computing term and give one practical example. 'bias'
when something is unfair towards or against something or someone. Add one real device or system example to prove understanding.
3. Correct this common computing misconception.
Mistake: A model can be improved by providing more training data. Correction: Although a large data set is important, more data doesn't necessarily improve output. It is important that training data is high-quality, accurate and diverse.
More Lessons In This Unit
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