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Visualize and Clean SKU Sales Data for Outliers

Boston Consulting GroupData Scientist · Technical Screen
MediumData ScientistTechnical ScreenData Manipulation (SQL/Python)

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This question evaluates data manipulation and exploratory data analysis skills, including aggregation, outlier detection (IQR/Z-score) and visualization using Python libraries such as pandas and matplotlib/seaborn.

sales_data

+------------+--------+-----------+----------+------------+---------+ | date | sku_id | unit_sold | revenue | promo_flag | store_id| +------------+--------+-----------+----------+------------+---------+ | 2023-01-01 | A123 | 120 | 2400.00 | 1 | S01 | | 2023-01-02 | A123 | 80 | 1600.00 | 0 | S01 | | 2023-01-01 | B456 | 200 | 3000.00 | 1 | S02 | | 2023-01-02 | B456 | 50 | 750.00 | 0 | S02 |

Scenario

Codesignal live-coding: analyst receives raw daily SKU sales data and must explore it visually while cleaning extreme values.

Question

Using Python (pandas, matplotlib/seaborn), draw a histogram of daily revenue per SKU and identify outliers with the IQR or Z-score method. Remove the detected outliers and re-plot the cleaned distribution.

Hints

Focus on reproducible pandas pipeline: load → aggregate → detect outliers → filter → visualize before/after.