Congratulations

1. Congratulations

Congratulations on completing the course on Calculations in Sigma! We hope you've learned some new techniques for extracting insights from your data. Let's review what we've learned.

2. Chapter 1

In chapter 1, we covered the basics of functions in Sigma. We learned how Sigma categorizes data and column types, and how Sigma's formula bar helps us ensure clean syntax. We also explored basic and aggregate If functions, learning how these functions can help us translate business logic into insightful new columns in our data. In the video on window functions, we introduced how Sigma treats multi-row, or moving calculations.

3. Chapter 2

In Chapter 2 we explored period-over-period analysis in Sigma and how the period over period comparison menu makes it quick and intuitive to add columns comparing one time period's results to another. We also learned how the InDateRange function can help us when our comparison timeframes are rolling or non-standard periods.

4. Chapter 3

Finally, in Chapter 3 we practiced lookups in Sigma. Critical for many calculations, lookups help us blend data from multiple tables, or even calculate row-level summary metrics on a single table, without needing to leverage code or complex join logic.

5. Congratulations

We hope you've enjoyed taking this course and uncovering new ways Sigma can empower you in your data analytics journey.

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This exercise is part of the course

Calculations in Sigma

BeginnerSkill Level

5.0+

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This chapter introduces the core building blocks that make Sigma calculations accurate and dependable. You’ll learn how syntax cues, data types, and logical expressions shape the results you compute. Then you’ll progress from IF and SWITCH logic to conditional aggregations and window functions to build calculations that answer real business questions.

Exercise 1: Sigma data and column types Exercise 2: Understanding Sigma function rules Exercise 3: Troubleshooting function errors and warnings Exercise 4: Basic IF functions in Sigma Exercise 5: Flagging customer groups with If functions Exercise 6: Assigning categories with nested If functions Exercise 7: Standardizing naming conventions with Switch Exercise 8: Aggregate IF functions Exercise 9: Building conditional KPIs with CountIf and AvgIf Exercise 10: Leveraging logical columns in calculations Exercise 11: Window functions Exercise 12: Recognizing window functions in Sigma Exercise 13: Calculating rolling interest rates with MovingAvg Exercise 14: Calculating cumulative sums on un-aggregated data

This chapter focuses on how Sigma streamlines period-over-period analysis for real business reporting. You’ll learn how to set up valid date fields, choose the right time granularity, and use Sigma’s automated MoM, QoQ, and YoY comparison tools. You’ll then expand into flexible techniques like DateLookBack and InDateRange to support rolling windows and custom time periods.

Exercise 1: What is Period-over-Period analysis?Exercise 2: Understanding the purpose of PoP analysis Exercise 3: Understanding what PoP analysis needs to work Exercise 4: Adding a quick PoP comparison to a grouped table Exercise 5: Sigma’s PoP (Period-over-Period) comparison menu Exercise 6: Calculating new loans approved since last quarter Exercise 7: Comparing growth of loan types year-over-year Exercise 8: InDateRange formula for custom PoP analysis Exercise 9: Identifying upcoming loan maturities Exercise 10: Trailing 12-month account openings

This chapter explores how Sigma’s Lookup tools let you enrich tables by bringing in data from other sources without writing SQL. You’ll learn when to use lookups instead of joins, how to configure them correctly, and how to return aggregated values through the Lookup wizard. You’ll also learn to troubleshoot multiple-match issues and perform multi-key lookups for complex datasets.

Exercise 1: Add Lookup modal Exercise 2: Understanding lookup requirements in Sigma Exercise 3: Configuring a Lookup in Sigma Exercise 4: Looking up account balances by customer Exercise 5: Labeling transactions with account type Exercise 6: Aggregating lookup results Exercise 7: Averaging credit scores for customer groups Exercise 8: Evaluating loan officer performance Exercise 9: Lookups with multiple key columns Exercise 10: Calculating monthly loan approval rates by type Exercise 11: Tracking customer credit journeys Exercise 12: Congratulations

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