Data Analyst – Take-Home Assignment
E-commerce analytics assignment asking the candidate to clean and validate an Orders_Data dataset, compute sales and customer KPIs, analyze performance by time/city/category/channel, assess relationships between delivery time, ratings, and returns, and produce a dashboard plus a short findings/recommendations summary.
Shopify
IQ TeamOverview
You are working as a junior data analyst for an e-commerce company. The business wants to understand sales performance, customer behaviour, returns, delivery performance, and channel efficiency.
Use the attached Orders_Data dataset to identify useful insights and recommend actions for management.
Suggested time: 2–3 hours
Tasks
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Clean and validate the dataset. Identify missing values, inconsistent categories, invalid values, and any assumptions you make.
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Calculate key KPIs such as:
- total orders
- total units
- gross sales
- net sales
- average order value
- return rate
- average customer rating
-
Analyse sales by:
- month
- city
- category
- sales channel
Identify the strongest and weakest performers.
-
Analyse whether delivery time and customer rating appear related to returns or business performance.
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Identify at least 3 actionable business insights and explain how management could use them to improve sales, customer experience, or operational efficiency.
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Create a simple dashboard or visual summary using Excel, Power BI, Tableau, Python, or another suitable analytics tool.
Deliverable
Submit exactly one PDF that includes all of the following:
- your cleaned-data validation summary, including missing values, inconsistencies, invalid values, and assumptions
- your KPI calculations and analysis
- your dashboard or visualizations
- a short 1–2 page summary of findings and business recommendations
- any SQL or Python code used, embedded in the PDF
If you use code, include it in the PDF in a form that is executable and can regenerate the analysis and outputs shown.
Evaluation Criteria
| Area | What the interviewer will assess |
|---|---|
| Data Cleaning | Accuracy, consistency, and handling of missing/invalid data |
| Analysis | Correct KPIs, logical analysis, and ability to identify patterns |
| Business Thinking | Quality and practicality of recommendations |
| Visualization | Clarity, relevance, and storytelling |
| Communication | Ability to explain assumptions, findings, and limitations |
There is no single correct business conclusion. Strong submissions clearly explain the data, assumptions, reasoning, and practical impact of their recommendations.