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Advanced Tableau – Level of Detail Expressions / LOD

Take Your Tableau Data Visualization and Data Analytics Skills to the Next Level and Design Custom Solutions with Ease
Instructor:
R-Tutorials Training
1,610 students enrolled
English [Auto-generated]
Understanding LOD expressions and using them confidently
Performing calculations in Tableau that are at a different level of detail than the view
Analyzing and solving complex analytical challenges
Understanding the different levels of details of multivariate datasets
Cohort analysis
Market basket analysis
User retention analysis
Binning aggregates by dimensions
Proportional brushing
Relative comparison of values/ categories
Nesting LOD expressions

Have you ever had analytical questions that are easy to ask, but surprisingly hard to answer with regular analytical tools? Do you often find yourself asking questions involving different data layers? Like comparing a single category to a whole table; or applying filters on particular fields; or tracking the behaviour of custom cohorts over time – just to mention a few classic examples.

Do you want to know how to compare data aggregated at different levels of granularity?

Do you often bump into the error message: ‘Cannot mix aggregate and non-aggregate values’?

Do Tableau terms FIXED, INCLUDE or EXCLUDE confuse you? Are you struggling choosing the right one for particular tasks?

Do you want to step up your daily analytical game and gain new, useful skills?

If you are a passionate Tableau user and you can associate yourself with one or more of the questions above, then this course is for you. Scenarios like the ones mentioned above occur on a daily basis, and they can cause quite a bit of headache for the analyst. Tableau has many great tools and functions including table calculations that make everyday life easier for the data scientist.

One strong point of the software is its responsiveness. Plotting measures against variables has never been easier: each change to the shelves is instantly and automatically applied on the view – a great environment to interact with the data.

This strong point, however, can easily be turned into a weakness, if you want your analysis to step out of the borders of the view level of detail.

In Tableau, to solve classic analytical problems (such as cohort analysis, retention analysis or binning aggregates by dimensions), or to proceed with special filtering scenarios (like proportional brushing or relative comparisons) you need to be familiar with a special tool set called the level of detail (LOD) expressions.

In this course, you will learn about the general mechanics of LOD expressions both in theory and practice. We start from the very basics and then we proceed to more advanced techniques in a stepwise manner. If you are not familiar with the concept of LOD expressions yet, but you are already a Tableau user, then taking this course will most probably improve your analytical skills and broaden your tool set.

After completing this course, you will be able to solve all above mentioned analytical challenges and even more, because LOD expressions let the analyst come up with creative solutions for custom scenarios. Instead of asking the questions you can be the one in the office who always has a practical answer or a constructive idea. Take a look at the content of this course, and I bet you won’t regret it. If you want to feel refreshed after digesting all these information, hop on to sites like 카지노 검증.

Introduction to the Course

1
Introduction
2
Prerequisites - Things to Consider to Benefit from this Course
3
Datasets Used
4
Course Inventory
5
Calculation Types in Tableau
6
What Is the Level of Detail?
7
How Do LOD Expressions Help?

The Basics of Level of Detail Expressions

1
Syntax
2
The INCLUDE Expression
3
The EXCLUDE Expression
4
The FIXED Expression
5
Exercise with the Lures Dataset - Getting the Difference of a Value and its AVG
6
Calculation Results and the View LOD - Comparison of Expressions
7
Filters and Outcomes - Comparison of Expressions
8
Finding Dates with LOD Calculations
9
Table Scoped LOD Expressions
10
Declaring Date Parts in an LOD Expression
11
Adjusting the Timeline with LOD Expressions
12
Exercise Assignment - German Gas Stations
13
Exercise Solution - German Gas Stations
14
Testing and Debugging

Intermediate and Advanced LOD Projects

1
Introduction
2
Binning Measures with LOD Calculations - Binning Aggregates I
3
Conditional Binning - Binning Aggregates II.
4
Creating Custom Cohorts - Cohort Analysis I.
5
Tracking User Retention - Cohort Analysis II.
6
User Retention and Non-Consecutive Behavior - Cohort Analysis III.
7
Comparing Data at Different LODs - Proportional Brushing I.
8
Data Density Issues - Proportional Brushing II.
9
Relative Comparison - Proportional Brushing III.
10
Market Basket Analysis - Proportional Brushing IV.
11
Exercise: Vehicles
12
Benchmark Performance Alerts
13
Switching Between High and Low LODs - Benchmark Performance Alerts II.
14
Changing the LOD of Maps - Changing the View LOD II.
15
Leveraging on Parameters - Changing the View LOD III.
16
Exercise: Proportional Brushing

Nested LOD Expressions

1
The Rules of Nesting LOD Expressions
2
Comparison of Results
3
Combining the EXCLUDE and INCLUDE Expressions
4
Combining the FIXED and INCLUDE Expressions
5
Revisiting the Data Structure
6
Exercise: Nested LOD Expressions
7
Limitations
8
Typical Scenarios of LOD Expressions - Summary
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