Tableau for Data Analytics (Short & Focused)
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SUBMISSION: Advanced Tools

Create a Tableau workbook that uses advanced features. Your submission should include: at least one parameter that lets viewers control the analysis (e.g., select a metric or date range), and at least one FIXED LOD expression (e.g., { FIXED [Dimension] : SUM([Measure]) }) to compute a metric at a different granularity. (Optional but encouraged: incorporate a Quick Table Calculation, e.g., a Running Total, to reflect all three Advanced Tools techniques taught in this module.) Export as a .twbx file.

34 minutesProject submission
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Assignment Instructions

📥 Data Sources

This assignment uses two related tables that you'll join in Tableau:

⬇ Customers (.csv) ⬇ Orders (.csv)

Customers: 200 customers with ID, Name, City, State, Region, Tier, Signup Date.
Orders: ~1,470 orders over 2 years with Customer ID, Product, Product Category, Channel, Quantity, Amount.
Join on Customer ID.


📋 Assignment Instructions

Create a Tableau workbook that uses parameters and LOD expressions to enable flexible analysis.

Part 1: Data Setup

  1. Connect both CSVs — join Customers and Orders on Customer ID (left join from Customers).
  2. Verify the join — check that customers without orders appear as nulls (they should — not all 200 customers have orders every month).

Part 2: Parameters (minimum 2)

  1. Metric Selector Parameter — create a string parameter with allowed values: "Revenue", "Orders", "Avg Order Value". Then create a calculated field that switches the measure based on the parameter:
    CASE [Metric Selector]
      WHEN "Revenue" THEN SUM([Amount])
      WHEN "Orders" THEN COUNTD([Order ID])
      WHEN "Avg Order Value" THEN SUM([Amount]) / COUNTD([Order ID])
    END
    Use this in a line chart so the user can toggle which metric to view over time.
  2. Top N Parameter — create an integer parameter (range 5–50, step 5). Use it with a RANK() calculation to show only the Top N customers by total spend:
    IF RANK(SUM([Amount])) <= [Top N Parameter] THEN 'Show' ELSE 'Hide' END
    Filter the view to "Show" and display in a bar chart.

Part 3: LOD Expressions (minimum 3)

💡 Step-by-step guide for the LOD exercises below:
LOD expressions use curly braces { } and the syntax { FIXED [Dimension] : AGG([Measure]) }. They compute values at a different granularity than your current view. Start each exercise by creating a new Calculated Field (right-click in the Data Pane → Create Calculated Field).

  1. Customer Lifetime Value — use a FIXED LOD to calculate each customer's total spend regardless of filters:
    { FIXED [Customer ID] : SUM([Amount]) }
    Show the distribution of CLV in a histogram.
  2. First Purchase Date — use FIXED LOD to find each customer's first order date:
    { FIXED [Customer ID] : MIN([Order Date]) }
    Create a "Customer Cohort" calculated field from this using: DATETRUNC('month', [First Purchase Date]). This gives you the year-month of each customer's first purchase.

    To build the cohort heat map:
    1. Drag [Customer Cohort] to Rows
    2. Drag [Order Date] (as discrete month) to Columns
    3. Drag COUNTD([Customer ID]) to the Color mark
    4. Change the chart type to a highlight table or square marks
  3. Customer vs Average Comparison — use a FIXED LOD to calculate each customer's regional average spend:
    { FIXED [Region] : AVG([Amount]) }
    Compare this to each customer's individual total spend. Color a scatter plot to show above/below average customers.
💡 Optional (not required): incorporate a Quick Table Calculation (e.g., a Running Total of spend over time) on one of your sheets to practice all three Advanced Tools techniques from this module.

✅ Submission Checklist

  • Two data sources joined on Customer ID
  • Metric Selector parameter with dynamic calculated field
  • Top N parameter (RANK-based) controlling a bar chart
  • 3 FIXED LOD expressions (CLV, First Purchase Date, regional comparison)

Export your workbook as a .twbx (packaged workbook) and upload using the form below.

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