Study guides
Data Science
2 guides for this subject — how to prepare, what to practise, and how to know it has gone in.
A practice question
Choose an answer and it is marked, with the explanation. From Data Science Practice Questions: 10 Worked Examples.
A team is building a model to predict whether a customer will miss a loan payment in the next 30 days. One feature, last_payment_status, is populated only after the payment deadline has passed. What is the best response?
A predictor must be available at the time the model is used. A status recorded after the deadline leaks information about the outcome; strong correlation does not make it valid. Keeping it out of training but using it in testing would also make evaluation inconsistent.
A spoken answer, marked
An examiner's question, answered out loud and marked criterion by criterion. From Data Science Viva Questions: Practise Three Technical Stations.
Examiner
A subscription company wants to identify customers likely to cancel in the next 30 days. How would you design a model and make sure its evaluation represents future use?
Defines a 30-day cancellation target and links the prediction to retention action.
ImproveSpecify the snapshot date and exactly which event counts as cancellation.
Recognises that post-snapshot events must not enter the features.
ImproveGive an example: exclude a cancellation-processing timestamp recorded after the prediction date.
Chooses a chronological holdout and proposes a simple baseline.
ImproveState that all feature construction must use information available at each snapshot.
Connects recall and precision to retention capacity but does not fully explain calibration.
ImproveDescribe how you would check whether predicted probabilities correspond to observed churn rates.
A strong answerI would define a prediction snapshot for each customer and label whether cancellation occurs in the following 30 days. Every feature must be available at that snapshot; I would exclude later information such as a cancellation-processing event or a support outcome recorded afterwards. I would compare a simple baseline with a more flexible model and use a chronological holdout, keeping feature construction inside each training period. I would report precision and recall at a threshold matched to the retention team’s capacity, and check calibration if the scores are presented as probabilities. I would also inspect performance across relevant customer groups and revisit the design if the intervention changes customer behaviour.
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Guides
- Data Science Practice Questions: 10 Worked ExamplesPractise data science questions on leakage, metrics, model evaluation, statistics, experiments and SQL, with worked explanations for every answer.
- Data Science Viva Questions: Practise Three Technical StationsPractise data science viva questions on leakage, imbalanced classification and model monitoring, with worked answers and a repeatable speaking method.
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