Advanced Tableau – Level of Detail Expressions / LOD
Advanced Tableau – Level of Detail Expressions / LOD, available at $84.99, has an average rating of 4.69, with 45 lectures, based on 904 reviews, and has 7555 subscribers.
You will learn about 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 This course is ideal for individuals who are Tableau users who want to unlock new analytical skills or Tableau users who want to better understand the usage of level of detail expressions or This course is not for absolute Tableau beginners It is particularly useful for Tableau users who want to unlock new analytical skills or Tableau users who want to better understand the usage of level of detail expressions or This course is not for absolute Tableau beginners.
Enroll now: Advanced Tableau – Level of Detail Expressions / LOD
Summary
Title: Advanced Tableau – Level of Detail Expressions / LOD
Price: $84.99
Average Rating: 4.69
Number of Lectures: 45
Number of Published Lectures: 45
Number of Curriculum Items: 45
Number of Published Curriculum Objects: 45
Original Price: $94.99
Quality Status: approved
Status: Live
What You Will Learn
- 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
Who Should Attend
- Tableau users who want to unlock new analytical skills
- Tableau users who want to better understand the usage of level of detail expressions
- This course is not for absolute Tableau beginners
Target Audiences
- Tableau users who want to unlock new analytical skills
- Tableau users who want to better understand the usage of level of detail expressions
- This course is not for absolute Tableau beginners
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.
Course Curriculum
Chapter 1: Introduction to the Course
Lecture 1: Introduction
Lecture 2: Prerequisites – Things to Consider to Benefit from this Course
Lecture 3: Datasets Used
Lecture 4: Course Inventory
Lecture 5: Calculation Types in Tableau
Lecture 6: What Is the Level of Detail?
Lecture 7: How Do LOD Expressions Help?
Chapter 2: The Basics of Level of Detail Expressions
Lecture 1: Syntax
Lecture 2: The INCLUDE Expression
Lecture 3: The EXCLUDE Expression
Lecture 4: The FIXED Expression
Lecture 5: Exercise with the Lures Dataset – Getting the Difference of a Value and its AVG
Lecture 6: Calculation Results and the View LOD – Comparison of Expressions
Lecture 7: Filters and Outcomes – Comparison of Expressions
Lecture 8: Finding Dates with LOD Calculations
Lecture 9: Table Scoped LOD Expressions
Lecture 10: Declaring Date Parts in an LOD Expression
Lecture 11: Adjusting the Timeline with LOD Expressions
Lecture 12: Exercise Assignment – German Gas Stations
Lecture 13: Exercise Solution – German Gas Stations
Lecture 14: Testing and Debugging
Chapter 3: Intermediate and Advanced LOD Projects
Lecture 1: Introduction
Lecture 2: Binning Measures with LOD Calculations – Binning Aggregates I
Lecture 3: Conditional Binning – Binning Aggregates II.
Lecture 4: Creating Custom Cohorts – Cohort Analysis I.
Lecture 5: Tracking User Retention – Cohort Analysis II.
Lecture 6: User Retention and Non-Consecutive Behavior – Cohort Analysis III.
Lecture 7: Comparing Data at Different LODs – Proportional Brushing I.
Lecture 8: Data Density Issues – Proportional Brushing II.
Lecture 9: Relative Comparison – Proportional Brushing III.
Lecture 10: Market Basket Analysis – Proportional Brushing IV.
Lecture 11: Exercise: Vehicles
Lecture 12: Benchmark Performance Alerts
Lecture 13: Switching Between High and Low LODs – Benchmark Performance Alerts II.
Lecture 14: Changing the LOD of Maps – Changing the View LOD II.
Lecture 15: Leveraging on Parameters – Changing the View LOD III.
Lecture 16: Exercise: Proportional Brushing
Chapter 4: Nested LOD Expressions
Lecture 1: The Rules of Nesting LOD Expressions
Lecture 2: Comparison of Results
Lecture 3: Combining the EXCLUDE and INCLUDE Expressions
Lecture 4: Combining the FIXED and INCLUDE Expressions
Lecture 5: Revisiting the Data Structure
Lecture 6: Exercise: Nested LOD Expressions
Lecture 7: Limitations
Lecture 8: Typical Scenarios of LOD Expressions – Summary
Instructors
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R-Tutorials Training
Data Science Education
Rating Distribution
- 1 stars: 11 votes
- 2 stars: 16 votes
- 3 stars: 81 votes
- 4 stars: 329 votes
- 5 stars: 467 votes
Frequently Asked Questions
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