"Try each question before checking — recalling an answer strengthens memory far more than re-reading."
Learning Objectives
By the end of this module, you will be able to:
- Test your understanding across the whole course
- Spot topics worth revisiting
- Reinforce key ideas through active recall
How to Use This Quiz
Answer all 14 first, then scroll to Answers & Explanations. Each miss points to the module worth another look.
These questions span the whole course, from "what is data science?" to deployed-model drift. They aren't meant to trick you. Treat a wrong answer as a gift: it points, precisely, at the module that will most improve your understanding.
Try this: Decide your answer before looking. Hesitating between two options shows you exactly what to review.
Explain like I'm new: This is active recall — pulling an answer from your own head instead of re-reading it. It feels harder than skimming notes, and that difficulty is exactly why it works: the effort of remembering carves the idea into long-term memory.
The Questions
1. Data science is best described as…
- A. Only building charts
- B. Turning raw data into insight and better decisions
- C. Writing websites
- D. Storing files
2. Which describes structured data?
- A. Emails and photos
- B. Neat rows and columns (a table)
- C. Random noise
- D. Video only
3. Which is most robust to outliers?
- A. Mean
- B. Median
- C. Range
- D. Sum
4. Correlation between two variables means…
- A. One causes the other
- B. They tend to move together
- C. They are identical
- D. Nothing
5. SQL is mainly used to…
- A. Draw charts
- B. Query relational databases
- C. Train neural networks
- D. Edit images
6. What usually takes the most time in a data project?
- A. Making the final slide
- B. Cleaning and preparing data
- C. Choosing a font
- D. Naming files
7. A bar chart is best for…
- A. Comparing amounts across categories
- B. Showing parts of a whole
- C. Showing a relationship
- D. Nothing
8. In supervised learning you have…
- A. No data
- B. Labeled examples with known answers
- C. Only images
- D. A finished model
9. Clustering is an example of…
- A. Supervised learning
- B. Unsupervised learning
- C. A database
- D. A chart
10. Why split data into training and testing sets?
- A. To save space
- B. To check the model works on unseen data
- C. It's required by Python
- D. To make it slower
11. Overfitting means a model…
- A. Is too simple
- B. Memorizes noise and fails on new data
- C. Runs fast
- D. Has no features
12. A key ethics concern in data science is…
- A. File size
- B. Bias and fairness
- C. Font choice
- D. Chart color
13. A misleading chart often…
- A. Starts a bar axis at zero
- B. Truncates the axis to exaggerate differences
- C. Has clear labels
- D. Uses the right chart type
14. "Drift" in a deployed model means…
- A. It moves on screen
- B. Its accuracy slips as the world changes
- C. It was deleted
- D. It got faster
Answers & Explanations
- B — Data science turns raw data into insight and decisions. (Module 1)
- B — Structured data fits neat rows and columns. (Module 4)
- B — The median resists outliers; the mean doesn't. (Module 5)
- B — Correlation means they move together, not that one causes the other. (Modules 5, 2)
- B — SQL queries relational databases. (Module 7)
- B — Cleaning and preparing data usually dominates. (Modules 2, 10)
- A — Bar charts compare amounts across categories. (Module 12)
- B — Supervised learning uses labeled examples. (Module 25)
- B — Clustering is unsupervised. (Module 29)
- B — Testing on unseen data checks real learning. (Module 30)
- B — Overfitting memorizes noise and fails to generalize. (Module 31)
- B — Bias and fairness are core ethics concerns. (Module 3)
- B — Truncated axes exaggerate differences. (Module 16)
- B — Drift is accuracy slipping as data changes. (Module 37)
How did you do?
- 12–14: Excellent — you understand the whole data science journey.
- 7–11: Strong — revisit the modules behind any misses.
- 0–6: Re-skim Modules 1, 5, and 25, then try again. You'll get there.
Whatever your score, notice which questions you missed. A miss on 1–2 or 4 means the foundations need another pass. Misses on 8–11 point to the machine-learning core. A slip on 13 is about honest visualization, and 14 about keeping deployed models healthy. The pattern of your misses is a personalized study plan.
Data scientist tip: Don't just re-read the module behind a miss — re-do the small example or callout in it. Explaining an idea in your own words, or trying its "Try this" task, cements it far better than re-reading.
Key Takeaway: These 14 questions span the course — what data science is, data types, statistics, SQL, cleaning, visualization, machine learning (supervised/unsupervised, training/testing, overfitting), ethics, honest charts, and MLOps drift. Any misses point straight to the module to revisit.