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.
- Price: $279.99
- Delivery Method: eLearning
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Test Prep
37+ Pre Assessment Questions |
38+ Post Assessment Questions |
Features
21+ LiveLab |
21+ Video tutorials |
01:48+ Hours
Why choose TOPTALENT?
- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
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Outline
Lessons 1:
Introduction
- Who this course is for?
- Organization of this course
- Conventions
Lessons 2:
Introduction to data modeling
- Working with a single table
- Introducing the data model
- Introducing star schemas
- Understanding the importance of naming objects
- Conclusions
Lessons 3:
Using header/detail tables
- Introducing header/detail
- Aggregating values from the header
- Flattening header/detail
- Conclusions
Lessons 4:
Using multiple fact tables
- Using denormalized fact tables
- Filtering across dimensions
- Understanding model ambiguity
- Using orders and invoices
- Conclusions
Lessons 5:
Working with date and time
- 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
Lessons 6:
Tracking historical attributes
- Introducing slowly changing dimensions
- Using slowly changing dimensions
- Loading slowly changing dimensions
- Rapidly changing dimensions
- Choosing the right modeling technique
- Conclusions
Lessons 7:
Using snapshots
- Using data that you cannot aggregate over time
- Aggregating snapshots
- Understanding derived snapshots
- Understanding the transition matrix
- Conclusions
Lessons 8:
Analyzing date and time intervals
- Introduction to temporal data
- Aggregating with simple intervals
- Intervals crossing dates
- Modeling working shifts and time shifting
- Analyzing active events
- Mixing different durations
- Conclusions
Lessons 9:
Many-to-many relationships
- Introducing many-to-many relationships
- Cascading many-to-many
- Temporal many-to-many
- Using the fact tables as a bridge
- Conclusions
Lessons 10:
Working with different granularity
- Introduction to granularity
- Relationships at different granularity
- Conclusions
Lessons 11:
Segmentation data models
- Computing multiple-column relationships
- Computing static segmentation
- Using dynamic segmentation
- Understanding the power of calculated columns: ABC analysis
- Conclusions
Lessons 12:
Working with multiple currencies
- Understanding different scenarios
- Multiple source currencies, single reporting currency
- Single source currency, multiple reporting currencies
- Multiple source currencies, multiple reporting currencies
- Conclusions
Appendix A. Data modeling 101
- Tables
- Data types
- Relationships
- Filtering and cross-filtering
- Different types of models
- Measures and additivity
