Turn shop orders into a decision brief.

Copy this fictional practice CSV into a spreadsheet or a local analysis tool. unit_price_inr is the item price; discount_inr is the discount for the whole row. Returned and cancelled rows do not contribute to completed sales. There is a duplicate order and a missing price to investigate.

order_id,date,category,quantity,unit_price_inr,discount_inr,status P001,2026-08-01,Stationery,2,120,0,completed P002,2026-08-01,Books,1,450,50,completed P003,2026-08-02,Stationery,3,80,0,completed P003,2026-08-02,Stationery,3,80,0,completed P004,2026-08-02,Bags,1,900,100,returned P005,2026-08-03,Books,2,300,0,completed P006,2026-08-03,Bags,1,850,0,cancelled P007,2026-08-04,Stationery,1,200,20,completed P008,2026-08-04,Books,1,,0,completed P009,2026-08-05,Bags,2,750,100,completed P010,2026-08-05,Books,1,500,0,completed P011,2026-08-06,Stationery,4,60,0,completed P012,2026-08-06,Books,1,350,0,returned

Keep a raw copy, define each field, and create a cleaning log. Remove the exact duplicate from the analysis copy. Flag the missing price instead of inventing it.

Submit a document, images, presentation or spreadsheet showing the data dictionary, cleaning decisions and excluded rows. Review criteria: raw data is preserved, duplicate handling is explicit, and missing values are not silently treated as zero.

Use your cleaned table from task 1. For completed rows with a known price, calculate row revenue as quantity × unit price − row discount. Calculate total known completed revenue, distinct completed orders with known prices, average order value for those orders, and revenue by category. Show all amounts in INR.

Keep returned, cancelled and unknown-price orders visible in a separate quality summary. Create one clearly labelled category chart and one table that reconciles to the overall total. Explain why this small six-day dataset cannot establish a long-term growth trend or profitability.

Submit a document, images, presentation or spreadsheet with formulas or code excerpts, calculations, chart and reconciliation. Review criteria: correct units and filters, consistent denominators, totals that agree and clear treatment of the missing order price. Include a read-only workbook or repository link if useful; the submission must contain enough evidence to review on its own.

Write a one-page decision brief using only findings supported by your calculations. Lead with the most useful finding, then recommend two practical follow-ups, such as resolving the missing price or collecting a longer observation period.

Distinguish observed values from hypotheses. Do not invent costs, margins, customer characteristics or reasons for returns. Add a short methods appendix so another analyst can reproduce the work.

Submit a document, images, presentation or spreadsheet: one page for the owner and one or two pages of methods. Review criteria: numerical claims match your analysis, recommendations follow from evidence, limitations are understandable and the reader can see what to do next. Label every chart and report as a KFIQ fictional practice dataset.