Work through the list without notes. If you cannot do a statement, follow its link, then come back. Guide = study guide; Practice = practice questions; see also the revision notes.
Understanding
- I can explain why collecting and storing personal data puts privacy at risk. (Guide: “1. Privacy”; Practice Question 1)
- I can define algorithmic bias using the words systemic, repeated and specific group. (Guide: “2. Algorithmic bias”; Practice Question 2)
- I can explain how a data collection method can introduce bias. (Guide: “3. How the data was collected”; Practice Question 3)
- I can explain how incomplete or inaccurate data can make a program work incorrectly. (Guide: “4. Data quality”; Practice Question 4)
Skills
- I can name a specific privacy safeguard that fits a scenario. (Guide: “1. Privacy”; Practice Questions 6 and 9)
- I can compare a sample’s make-up with the population it should represent. (Guide: Worked example 1; Practice Question 3)
- I can trace a program that mishandles a missing-value code and say what it should do instead. (Guide: Worked example 2; Practice Questions 4 and 8)
- I can write a balanced answer that names who is affected, why, and one remedy. (Guide: Worked example 1; Practice Question 7)
Reasoning
- I can decide whether a data set is appropriate for a particular question. (Guide: “4. Data quality and the right data set”; Practice Questions 5 and 9)
- I can tell algorithmic bias from a one-off bug. (Guide: “Common misconceptions”; Practice Question 2)
- I can explain why a larger data set does not by itself remove bias. (Guide: “Common misconceptions”; Practice Question 7)
All ticked? Move on to the Topic 4.2 study guide, Introduction to Using Data Sets, or return to the course roadmap.