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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.
Test Prep
37+ Pre Assessment Questions | 38+ Post Assessment Questions |
21+ LiveLab | 21+ Video tutorials | 01:48+ Hours

Why choose TOPTALENT?


Lessons 1:

  • 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