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29. Practice Exercises and Final Assignment

29.8 Modules 14–15: Visuals, Formatting and Maps

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  1. Build one example of every chart in the Module 14.3 table using our data, and write one sentence per chart on why it fits the question.
  2. Format a bar chart with the 10-click routine (Module 14.2) and copy the format to three visuals with Format painter.
  3. Build a line and column chart of Orders and Avg Delivery Time by Order Hour (IST). Which hours look busiest in your sample data? Compare a normal week with the Diwali week.
  4. Build a waterfall of Orders from September to October broken down by City.
  5. Build a scatter chart of stores (Orders vs Avg Delivery Time) with a play axis by month and constant lines for quadrants.
  6. Build a KPI visual for Avg Delivery Time with direction Low is good.
  7. Add a forecast (30 days) and an average line to a daily orders line chart.
  8. Use the decomposition tree to explain Total Orders by City › Area › Category.
  9. Enable map visuals, set data categories, and build a dark store bubble map with latitude/longitude, bubble size = Orders and conditional colours.
  10. Explain why a Map Location column like "Mumbai, Maharashtra, India" reduces geocoding errors.

Practice files (fictional): visuals_orders_apr_may_2026.csv, dark_stores_maharashtra.csv, daily_orders_nagpur_apr2026.csv

Hints
  • Start every visual from the question (Module 14.3).
Solutions (checked on the practice files)
  1. One sentence per chart: e.g. bar for City comparison (Pune ₹850 highest).
  2. Format painter copies formatting, not fields.
  3. Hourly data is not in the small file; on the Nagpur daily file the busiest day is Fri 10-Apr (236 orders).
  4. Apr → May waterfall on the visuals file: 900 − 350 (Pune) + 200 (Nashik) + 450 (Kolhapur) = 1,200.
  5. Scatter by store: Rajarampuri 7.0 min, College Road 10.0, Kothrud 12.0, Baner 12.0, Gangapur 14.0.
  6. KPI: Avg delivery 11.0 min; with Low is good, lower than target shows green.
  7. Forecast needs a continuous date axis (Analytics pane › Forecast).
  8. Decomposition tree: Total Sales ₹2,100 → Pune ₹850 → Kothrud ₹650.
  9. Use Latitude/Longitude from dark_stores_maharashtra.csv; set data categories.
  10. A full place string removes ambiguity (several places share a name).

Ravindra Bagale's Tip

Friends, many students finish the visuals exercise with default titles and colours. Treat each exercise like a real report: meaningful titles, sorted axes, consistent colours and no unnecessary gridlines. Try it once more, then move on.