Duration : 30 days | 1hr theory/day 1hr Practical


Introduction to Power BI


Learning Objective: This module will introduce you to Power BI, its Building Blocks
and the various fundamental concepts of Power BI.




Overview of BI concepts
Why we need BI ?
Introduction to SSBI
SSBI Tools
Why Power BI?
What is Power BI?
Building Blocks of Power BI
Getting started with Power BI Desktop


Explore Power BI Desktop
First Report on Power BI Desktop


Power BI Desktop

Learning Objective: This module will introduce you to Power BI Desktop, extract data from various  sources and establish connections with Power BI Desktop, perform Transformation operations on  data and know the Role of Query Editor in Power BI.



Power BI Desktop
Extracting data from various sources
Workspaces in Power BI
Data Transformation
Measures and Calculated Columns
Query Editor



Install Power BI Desktop | Data Transformation | Using Query Editor


Data Analysis Expressions (DAX)

Learning Objective: This module will help you understand the relationships between data tables, and you will learn the following - DAX Calculation Types, DAX Functions for Advanced Calculations, Time Intelligence Functions, Variables in DAX expressions and Table Relationships in DAX




Modelling Data
Manage Data Relationship
Optimize Data Models
What is DAX?
Data Types in DAX
Calculation Types
DAX Functions: Date and Time, Time Intelligence,
Information, Logical, Mathematical, Statistical, Text and Aggregate
Measures in DAX



Create relationships | Data Types in DAX | Calculated Columns
Explore DAX Functions such as Aggregation, Counting, Logical, Information, Text
Time Intelligence Functions | Creating Calculated Measure


Data Visualization


Learning Objective: This module will help you understand the benefits of Data Visualization, Best Practices of Data Visualization, Power BI Desktop Visualization, Custom Visuals in Power BI, Formatting Visuals and create Charts, Score Cards and other Visualization items in Power View




How to use Visual in Power BI ?
Charts in Power BI
Matrixes and tables
Map Visualizations
Gauges and Single Number Cards
R Visuals in Power BI
What Are Custom Visuals ?
KPI Visuals
Data Binding
Power BI report server


Introduction to Power BI Q&A and Data Insights

Learning Objectives: This module covers Power BI Q&A which is currently available as part of the Power BI for Office 365 Preview. You will also learn about Dashboards, Reports, Tiles Quick Insights and Power BI Publisher.




Why Dashboard?
Dashboard vs Reports
Creating Dashboards
Configuring a Dashboard: Dashboard Tiles, Pinning Tiles
Power BI Q&A
Quick Insights in Power BI
Power BI embedded



Explore Power BI Q&A | Creating a Dashboard | Run Quick Insights on a Dataset
Connect to Power BI Data in Excel
Pinning a Tile to a Dashboard | Pin a range to a Dashboard
Monitor Real-time Data with REST API

Direct Connectivity

Learning Objectives: In this module, you will be connecting directly to Azure, HD Spark, My SQL, and create interactive dashboards.


Custom Data Gateways
Exploring live connections to data with Power BI
Connecting directly to SQL Azure, HD Spark, and SQL Server Analysis Services/ My SQL
Introduction to Power BI Development API
Excel with Power BI: Connect Excel to
Power BI, Power BI Publisher for Excel
Content packs
Update content packs

Connecting Power BI with Azure, HD Spark, SSAS
Connecting Excel to Power BI
Use the developer API to create custom visuals
Creating content packs


Integrating Power BI and Azure ML

Learning Objectives: In this module, you will learn to integrate Azure Machine Learning model inside Power BI. Using Azure ML, Power BI regularly brings in the latest output of business model for analysis.




Extracting data out of Azure SQL using R
Using R, call the Azure ML web service and send it the unscored data
Writing the output of the Azure ML model back into SQL
read scored data into Power BI using R
Publishing the Power BI file to the Power BI service
Scheduling a refresh of the data using the Personal Gateway




Extracting data from Azure SQL using R
Writing the Output of the Azure ML back to SQL
Publishing the file to Power BI service | Calling Azure ML web service using R
Reading scored data into Power BI | Scheduling a refresh of the data




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