MLS-C01 Exploratory Data Analysis Practice Question
A data analyst is performing EDA on a dataset containing timestamps of user logins. They want to understand daily login patterns. The timestamp column is in Unix epoch format (integer). Which of the following is the most appropriate transformation to extract day-of-week patterns?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Convert the timestamps to datetime objects and extract the day-of-week.
Converting Unix epoch timestamps to datetime objects allows extraction of the day-of-week using functions like .dt.dayofweek() in pandas. Option B is wrong because converting to string and splitting into date and time does not directly give day-of-week patterns and loses temporal properties. Option C is wrong because min-max scaling is used for normalizing numerical features, not for extracting temporal patterns. Option D is wrong because binning into 1-hour intervals captures hourly patterns, not day-of-week patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Convert the timestamps to datetime objects and extract the day-of-week.
Why this is correct
This enables grouping by day of the week to analyze patterns.
- ✗
Convert the timestamps to string and split into date and time.
Why it's wrong here
String manipulation is less efficient and not directly analytical.
- ✗
Apply min-max scaling to the timestamp values.
Why it's wrong here
Scaling does not preserve temporal meaning.
- ✗
Bin the timestamps into 1-hour intervals.
Why it's wrong here
Hourly bins do not directly show day-of-week patterns.
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