Calculations in Sigma

Overview

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Build dynamic Sigma calculations to explore data, automate logic, and uncover trends with practical business examples.

Boost your analytics skills with the Calculations in Sigma course. You’ll learn to create dynamic, business-ready calculations that help teams explore data, automate logic, and surface meaningful trends. We’ll cover single-row logic with IF and SWITCH, conditional aggregations like SUMIF and AVGIF, rolling and cumulative analysis using window functions, and period-over-period comparisons to track performance over time.

You’ll also learn how to enrich datasets using Sigma’s Lookup modal and understand how Sigma handles data types, grouped tables, and syntax. By the end, you’ll be ready to build accurate, efficient calculations and apply them to real business scenarios in Sigma.

Syllabus

Foundations of Sigma Calculations

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.

Period-Over-Period Analysis

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.

Enriching Data with Lookups

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.