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Data Analysis Course: PowerPivot in Excel and Power BI

Learn to effectively analyze, visualize, and report on data using Excel, Power BI, and Power Pivot to make informed business decisions. 

(PWRBI-EXCEL.AP1) / ISBN : 978-1-64459-388-2
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About This Course

Get your hands dirty with data analysis using the Power BI app and Power Pivot tool within Excel. This hands-on training course arms you with skills to analyze, visualize, and report information. You’ll learn to connect various data sources, build data models, create dashboards, and perform advanced analysis techniques that will spot hidden insights. With this “Analyzing data with Power BI” course, you’ll make decisions to improve your business processes and get ahead of the competition in the data-centric industry.

Skills You’ll Get

  • Become proficient in using Excel, Power Pivot, and Power BI for data analysis and data visualization
  • Connect various data sources, such as Excel, SQL Server, CSV files, and online databases
  • Learn to create effective star and snowflake schemas, establish relationships, and implement hierarchies 
  • Utilize DAX (Data Analysis Expressions) to create calculated columns, measures, and perform complex calculations
  • Develop visually appealing and informative dashboards and reports to communicate insights effectively 
  • Understand and apply various data analysis techniques, including time intelligence, data cleansing, and performance optimization 
  • Integrate Power BI with other tools and technologies, such as SQL Server and Python
Download Course Outline

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

1

Introduction to data modeling

  • Exploring a Dataset
2

Using header/detail tables

  • Aggregating Values from the Header Table
3

Using multiple fact tables

  • Analyzing Denormalized Fact Tables
  • Understanding Model Ambiguity
4

Working with date and time

  • Creating a Date Dimension
5

Tracking historical attributes

  • Analyzing Slowly Changing Dimensions
6

Using snapshots

  • Analyzing Snapshots
  • Analyzing Derived Snapshots
  • Understanding the Transition Matrix
7

Analyzing date and time intervals

  • Understanding Temporal Data
  • Analyzing Events that Cross Dates
  • Analyzing Active Events
  • Mixing Different Durations
8

Many-to-many relationships

  • Exploring Many-to-Many Relationships
  • Exploring a Temporal Many-to-Many Relationship
9

Working with different granularity

  • Analyzing Relationships at Different Granularity
10

Segmentation data models

  • Analyzing Calculated Physical Relationships
  • Analyzing Dynamic Segmentation
  • Understanding ABC Analysis
11

Working with multiple currencies

  • Producing a Report Containing Information With a Single Type of Currency
  • Producing a Report in Multiple Currencies

Any questions?
Check out the FAQs

Check out our FAQ section for detailed answers.

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Power BI is a business intelligence tool for analyzing and visualizing data, while Power Pivot is a data modeling engine within Excel.

Power Pivot is used to create data models, and Power BI is used to build interactive dashboards and reports based on those models.

Basic knowledge of Excel is recommended for our Power BI and Excel for data analysis training, but no prior experience with Power BI or Power Pivot is necessary.

Learning Power BI and Power Pivot can enhance your skills in data analysis and visualization, making you an attractive candidate in the job market.

Develop Data Analytic and Visualization Skills

  Use Excel Power Pivot and Power BI integrations to analyze data and transform it into compelling visuals to tell a story. 

$ 448.68

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