MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a time series dataset of daily website visits. The scientist wants to identify any seasonality patterns. Which visualization is most appropriate?
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
✓
Line plot with day on x-axis and visits on y-axis, highlighting weekends.
A line plot with day on the x-axis and visits on the y-axis, with weekends highlighted, can reveal weekly seasonality patterns. Option A (correlation matrix with lags) can detect autocorrelation but is not a direct visualization of seasonality. Option B (scatter plot vs day of month) could show monthly patterns but is less effective for daily seasonality and does not preserve time order as clearly as a line plot. Option C (histogram) shows distribution, not temporal patterns. Therefore, option D is best.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Correlation matrix of visits with lagged versions of itself.
Why it's wrong here
A correlation matrix of lagged versions quantifies linear autocorrelation at discrete offsets but does not reveal periodic patterns across multiple cycles, such as weekly or monthly seasonality. This approach is tempting because it can detect repeating relationships between a time series and its past values, making it correct for identifying autoregressive dependencies rather than seasonal cycles.
- ✗
Scatter plot of visits against the day of the month.
Why it's wrong here
May show patterns but line plot is better for seasonality.
- ✗
Histogram of daily visit counts.
Why it's wrong here
Shows frequency, not time patterns.
- ✓
Line plot with day on x-axis and visits on y-axis, highlighting weekends.
Why this is correct
Reveals periodic patterns over time.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is performing EDA on a time series dataset of daily sales. The data scientist observes a pattern that repeats every 7 days. Which characteristic of the time series is being observed?
hard- A.Stationarity
- B.Autocorrelation
- ✓ C.Seasonality
- D.Trend
Why C: A pattern that repeats at a fixed frequency (every 7 days) is called seasonality. Option A is wrong because trend is a long-term increase or decrease. Option C is wrong because autocorrelation measures correlation with lagged values, not a repeating pattern. Option D is wrong because stationarity refers to constant mean/variance over time.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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