uCertify

Analyzing Data with Power BI and Power Pivot for Excel Course

(PWRBI-EXCEL.AP1.E0T)
Lessons
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Skills You’ll Get

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1

Introduction

  • Who this course is for?
  • Organization of this course
  • Conventions
2

Introduction to data modeling

  • Working with a single table
  • Introducing the data model
  • Introducing star schemas
  • Understanding the importance of naming objects
  • Conclusions
3

Using header/detail tables

  • Introducing header/detail
  • Aggregating values from the header
  • Flattening header/detail
  • Conclusions
4

Using multiple fact tables

  • Using denormalized fact tables
  • Filtering across dimensions
  • Understanding model ambiguity
  • Using orders and invoices
  • Conclusions
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
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
7

Using snapshots

  • Using data that you cannot aggregate over time
  • Aggregating snapshots
  • Understanding derived snapshots
  • Understanding the transition matrix
  • Conclusions
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
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
10

Working with different granularity

  • Introduction to granularity
  • Relationships at different granularity
  • Conclusions
11

Segmentation data models

  • Computing multiple-column relationships
  • Computing static segmentation
  • Using dynamic segmentation
  • Understanding the power of calculated columns: ABC analysis
  • Conclusions
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
A

Appendix A. Data modeling 101

  • Tables
  • Data types
  • Relationships
  • Filtering and cross-filtering
  • Different types of models
  • Measures and additivity

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