Analyzing Data with Power BI and Power Pivot for Excel
(PWRBI-EXCEL.AP1)
/ ISBN: 978-1-64459-388-2
This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)
Analyzing Data with Power BI and Power Pivot for Excel
Gain a hands-on experience in Power BI and Power Pivot for Excel and learn how to analyze data with the Analyzing Data with Power BI and Power Pivot for Excel course and lab. This course aims to teach you the basic concepts of data modeling through practical examples that you are likely to encounter in your daily life. This course will be beneficial for an Excel user who uses Power Pivot for Excel, a data scientist using Power BI, or even for those who want to read an introduction to the topics of data modeling.
Lessons
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13+ Lessons
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87+ Exercises
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50+ Quizzes
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43+ Flashcards
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43+ Glossary of terms
TestPrep
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37+ Pre Assessment Questions
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38+ Post Assessment Questions
LiveLab
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21+ LiveLab
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21+ Video tutorials
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01:48+ Hours
- Who this course is for?
- Organization of this course
- Conventions
- Working with a single table
- Introducing the data model
- Introducing star schemas
- Understanding the importance of naming objects
- Conclusions
- Introducing header/detail
- Aggregating values from the header
- Flattening header/detail
- Conclusions
- Using denormalized fact tables
- Filtering across dimensions
- Understanding model ambiguity
- Using orders and invoices
- Conclusions
- Creating a date dimension
- Understanding automatic time dimensions
- Using multiple date dimensions
- Handling date and time
- Time-intelligence calculations
- Handling fiscal calendars
- Computing with working days
- Handling special periods of the year
- Working with weekly calendars
- Conclusions
- Introducing slowly changing dimensions
- Using slowly changing dimensions
- Loading slowly changing dimensions
- Rapidly changing dimensions
- Choosing the right modeling technique
- Conclusions
- Using data that you cannot aggregate over time
- Aggregating snapshots
- Understanding derived snapshots
- Understanding the transition matrix
- Conclusions
- Introduction to temporal data
- Aggregating with simple intervals
- Intervals crossing dates
- Modeling working shifts and time shifting
- Analyzing active events
- Mixing different durations
- Conclusions
- Introducing many-to-many relationships
- Cascading many-to-many
- Temporal many-to-many
- Using the fact tables as a bridge
- Conclusions
- Introduction to granularity
- Relationships at different granularity
- Conclusions
- Computing multiple-column relationships
- Computing static segmentation
- Using dynamic segmentation
- Understanding the power of calculated columns: ABC analysis
- Conclusions
- Understanding different scenarios
- Multiple source currencies, single reporting currency
- Single source currency, multiple reporting currencies
- Multiple source currencies, multiple reporting currencies
- Conclusions
- Tables
- Data types
- Relationships
- Filtering and cross-filtering
- Different types of models
- Measures and additivity
Hands on Activities (Live Labs)
- Exploring a Dataset
- Aggregating Values from the Header Table
- Analyzing Denormalized Fact Tables
- Understanding Model Ambiguity
- Creating a Date Dimension
- Analyzing Slowly Changing Dimensions
- Analyzing Snapshots
- Analyzing Derived Snapshots
- Understanding the Transition Matrix
- Understanding Temporal Data
- Analyzing Events that Cross Dates
- Analyzing Active Events
- Mixing Different Durations
- Exploring Many-to-Many Relationships
- Exploring a Temporal Many-to-Many Relationship
- Analyzing Relationships at Different Granularity
- Analyzing Calculated Physical Relationships
- Analyzing Dynamic Segmentation
- Understanding ABC Analysis
- Producing a Report Containing Information With a Single Type of Currency
- Producing a Report in Multiple Currencies
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