Flipkart Data Analyst Interview Questions
Flipkart Data Analyst Interview Questions – Rounds
Prepare for Flipkart Data Analyst Interview Questions
| Category | Examples of Questions |
|---|---|
| SQL Questions | – Find the total number of orders placed in the last month. – Extract distinct values from a column in a table. – Retrieve a list of products that have never been purchased using products and orders tables. – Calculate the average order value per customer. – Find the top 5 best-selling products in the last year. – Retrieve customers who placed more than 3 orders. – Use a window function to rank products by their sales within each category. – Calculate a running total of sales for each day. – Categorize orders based on the total amount spent (e.g., “Low”, “Medium”, “High”). – Replace null values in a column with a default value. |
| Excel/Spreadsheets | – Create a pivot table to summarize sales by region and product category. – Use a pivot table to analyze monthly sales trends. – Use VLOOKUP to merge two datasets. – Write a formula to calculate the average of a dataset excluding outliers. – Clean and remove duplicates from a dataset in Excel. – Use conditional formatting to highlight outliers in a sales dataset. – Create a line chart to show monthly growth in customer orders. – Create a histogram to visualize product sales distribution. |
| Statistics | – If a product is purchased 20% of the time, what is the probability it will be purchased exactly 3 times out of 10 trials? – What is the probability of two independent events occurring simultaneously? – Calculate the mean, median, mode, and standard deviation of a dataset. – Handle a dataset with extreme outliers. – What is a p-value, and how is it used in hypothesis testing? – Set up and conduct an A/B test for a new feature. – Differentiate between a normal and skewed distribution. – Explain the Central Limit Theorem’s significance. |
| Data Interpretation & Case Study | – Identify the top-performing regions for a new product launch using a dataset of customer orders. – Analyze customer churn data and suggest metrics to present findings. – Analyze the impact of a new discount policy on sales revenue. – Forecast future sales trends using sales data over several months. – Visualize customer purchase patterns using a line chart or bar graph. – Present category-wise sales data and justify the visualization type chosen. |
| Python (Optional) | – Write a Python script to load a CSV file and calculate total sales per product. – Filter a DataFrame to show rows where the order value exceeds ₹1000. – Find missing values in a dataset and replace them with the column mean. – Merge two datasets in Python and remove duplicates. – Create a bar chart to show monthly sales distribution. – Create a scatter plot to visualize the relationship between product price and quantity sold. |
| Problem-Solving | – Analyze declining weekend orders and propose solutions. – Identify patterns in customer browsing behavior and suggest engagement-improving features. |
| Data Cleaning | – Handle missing values in a dataset using different methods. – Identify and remove outliers in a dataset. – Validate the accuracy and completeness of a dataset before using it for analysis. |
| Query Optimization | – If a product is purchased 20% of the time, what is the probability it will be purchased exactly 3 times out of 10 trials? – What is the probability of two independent events occurring simultaneously? – Calculate the mean, median, mode, and standard deviation of a dataset. – Handle a dataset with extreme outliers. – What is a p-value, and how is it used in hypothesis testing? – Set up and conduct an A/B test for a new feature. – Differentiate between a normal and skewed distribution. – Explain the significance of the Central Limit Theorem. |
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