LOD expressions - facts & myths

Understanding the data granularity and learning Tableau's LOD expressions is crucial in any analysis aiming to get beyond the obvious. They can be tricky when it comes to the syntax and surely require a lot of practice!

Let's see if you can correctly identify the TRUE and FALSE statements about data granularity and LOD Expressions, based on your knowledge so far.

Questo esercizio fa parte del corso

Calculations in Tableau

Visualizza il corso

Esercizio pratico interattivo

Passa dalla teoria alla pratica con uno dei nostri esercizi interattivi

Inizia esercizio

Questo esercizio fa parte del corso

Calculations in Tableau

AvançadoNível de habilidade

4.8+

496 reviews

Inizia il corso gratis

We will begin our journey through Tableau calculations with a concept of calculated fields and we will revisit the foundations of various types of calculations, such as row-level and aggregations. We will also learn how to troubleshoot typical calculations errors and, in the second part of this chapter, we will study useful String and Logical functions which are indispensable in any data analysis.

Exercise 1: Introduction to calculations Exercise 2: Calculations knowledge check Exercise 3: Start to calculate in Tableau Exercise 4: Basic calculations Exercise 5: Reusing calculations Exercise 6: Troubleshooting basic errors Exercise 7: Understanding the averages Exercise 8: String and Logical functions Exercise 9: Knowledge check Exercise 10: Practical use of string and logical functions Exercise 11: String operations Exercise 12: IF only this was the last exercise

In this chapter we will explore the navigation between various levels of data granularity. With LOD expressions we will learn how to perform calculations independent of the dimensions in the view. Prepare to discover a whole new world of analytical possibilities that will help you get beyond the obvious insights!

Exercise 1: Introduction to LOD Expressions and FIXED Exercise 2: LOD expressions - facts & myths

Esercizio in corso

Exercise 3: LOD FIXED in practice Exercise 4: Total distance per category Exercise 5: Cohort analysis Exercise 6: Survival rate Exercise 7: INCLUDE and EXCLUDE LOD expressions Exercise 8: LOD expressions knowledge check Exercise 9: INCLUDE and EXCLUDE expressions in practice Exercise 10: Practicing INCLUDE LOD Exercise 11: Practicing EXCLUDE LOD Exercise 12: Using multiple LOD expressions

In this chapter, we will zoom in on another useful type of calculations: Table calculations. We will learn how to calculate running totals, percentages of category, and ranking and discover ins and outs of underlying functions. In the second part of this chapter, we will uncover the parameters and learn how to use them in calculations. Let's go!

Exercise 1: Introduction to Table Calculations Exercise 2: Table Calculations fact check Exercise 3: Table Calculations in action Exercise 4: Cumulative calculation Exercise 5: Nested Table Calculation Exercise 6: Ranking Exercise 7: Introduction to parameters Exercise 8: Parameters knowledge check Exercise 9: Parameters in practice Exercise 10: Calculating TOP N Exercise 11: Switching measures Exercise 12: Parameterized quadrant chart

In this chapter, we will work on time series analysis, but before we do so, we will focus on date and time data types as the critical components of all relevant time series calculations. Learning the key date functions will empower us to progress towards more complex analysis such as computing CAGR, Year over year calculations, and exploring seasonality trends or indexed growths. Knowledge from all the preceding chapters will come in handy.

Exercise 1: Date and time functions Exercise 2: Working with date and time - knowledge check Exercise 3: Date and time in practice Exercise 4: Date conversions Exercise 5: Date differences Exercise 6: Time series analysis Exercise 7: Time series analysis - knowledge check Exercise 8: Time series analysis in practice Exercise 9: Seasonality and moving averages Exercise 10: Calculating CAGR Exercise 11: Indexed growth Exercise 12: Congratulations!