Home » Data Visualisation with Power BI

Data Visualisation with Power BI

Discover the art of storytelling with data in our Data Visualization with Power BI course! This program teaches you to create dynamic, insightful dashboards and visualizations using Power BI, making complex data easy to understand. You’ll learn to transform raw data into compelling visuals, enhancing your analytical skills and making data-driven insights accessible. Ideal for beginners, this course prepares you to bring data to life for impactful decision-making.

This course is a part of Data Analytics Program.

$700

Enrol Now

15-Module Program

Perfect for beginners and professionals alike!

Live, Instructor-Led

Learn from experienced instructors in real-time.

20-Hour Course

6 Hours/Week, Weekend classes

Online Classes

Join the course from anywhere. Save on travel-time.

Course Modules

Module 01 - Introduction to Power BI Desktop

The Microsoft Self-Service Business Intelligence Solution
Installing and Running Power BI Desktop
A First Power BI Desktop Dashboard
The Data Load Process
The Power BI Desktop Window

Module 02 - Discovering and Loading Data with Power BI Desktop

Data Sources
Loading Data (Web Pages, CSV Files, Text Files, XML Files, Excel, Microsoft Access Databases)
Connecting to Cloud (Azure)
Relational Databases: SQL Server
Extracting Data from the Web
Automatically Loading Related Tables
Database Options
Other Data Sources
Reusing and Pinning Data Sources
Refreshing Data
Connection Security

Module 03 - Transforming Datasets

Power BI Desktop Queries
Editing Data After a Data Load
Transforming Data Before Loading
Query or Load?
The Power BI Desktop Query Editor
The Applied Steps List
Grouping & Aggregating, Pivoting & Unpivoting
Dataset Shaping (Renaming Columns, Reordering Columns, Removing Columns, Merging Columns, Removing Records)
Removing Duplicates, Sorting Data, Filtering Data

Module 04 - Data Cleansing

Viewing a Full Record
Power BI Desktop Query Editor Context Menus
Changing Data Type
Detecting Data Types
Replacing Values
Transforming Column Contents
Filling Down
Using the First Row as Headers
Grouping Records

Module 05 - Data Mashup

The Power BI Desktop View
Extending
Duplicating (Splitting Columns, Merging Columns, Custom Columns, Index Columns, Merging Data, Appending Data)
Adding the Contents of One Query to Another
Adding Multiple Files from a Source Folder
Changing the Data Structure (Unpivoting Tables, Pivoting Tables)
Transposing Rows and Columns
Managing the Transformation Process (Managing Queries, Organizing Queries, Grouping Queries, Duplicating Queries, Referencing Queries)
Add a Column as a New Query
Enable Data Load
Enable Data Refresh
Pending Changes
Copying Data from Power BI Desktop

Module 06 - Creating a Data Model

Data Modeling in the Power BI Desktop Environment
Data Model or Query?
The Power BI Desktop Data View Ribbons
Managing Power BI Desktop Data
Power BI Desktop Data Types
Formatting Power BI Desktop Data
Preparing Data for Dashboards
Different Types of Schema (STAR, SNOWFLAKE, etc.)
Table Relationships & Cardinality
Database Normalization (Categorize Data, Apply a Default Summarization)
Sorting Data in Power BI Desktop Tables
Adding Hierarchies
Designing a Power BI Desktop Data Model

Data View and Relationship View
Relationship View Display Options
Creating Relationships
Creating Relationships Manually
Creating Relationships Automatically
Deleting Relationships
Managing Relationships
Deactivating Relationships
Advanced Relationship Options

Module 07 - Extending the Data Model with Calculated Columns

Types of Calculations
New Columns
Concatenating Column Contents
Tweaking Text
Simple Calculations

Math Operators
Rounding Values

Calculating Across Tables
Cascading Column Calculations
Using Functions in New Columns
Simple Logic: the IF() Function
Making Good Use of the Formula Bar

Exception Indicators
Creating Alerts
Flagging Data
Nested IF() Functions

Introducing DAX with Calculated Fields

Creating Custom Groups Using Multiple Nested IF() Statements
Multiline Formulas
Complex Logic
Formatting Logical Results
Comparing Data from Previous Years
Comparing with the Same Date Period from a Different Quarter, Month, or Year
Rolling Aggregations over a Period of Time

Module 08 - Adding Measures to the Data Model

A First Measure: Number of Cars Sold
Basic Aggregations in Measures
Using Multiple Measures
Cross-Table Measures
More Advanced Aggregations
Filter Context

Row Context
Query Context
Filter Context

Filtering Data in Measures

Simple Filters

Text and Numeric Filters
More Complex Filters
Multiple Criteria in Filters
Using Multiple Filters

Calculating Percentages of Totals

A Simple Percentage
Removing Multiple Filter Elements
Visual Totals
The ALLEXCEPT() Function

Filtering on Measures
Displaying Rank
A Few Comments and Notes on Using Measures
Calculation Options

Module 09 - Analyzing Data over Time

Simple Date Calculations
- Date and Time Formatting
- Calculating the Age of Cars Sold
- Calculating the Difference Between Two Dates
Adding Time Intelligence to a Data Model
Creating and Applying a Date Table
- Creating the Date Table
- Adding Sort By Columns to the Date Table
- Date Table Techniques
- Adding the Date Table to the Data Model
- Applying Time Intelligence
  - YTD, QTD, and MTD Calculations
  - Analyze Data As a Ratio over Time
  - Comparing a Metric with the Result from a Range of Dates
- Comparisons with Previous Time Periods
- Comparison with a Parallel Period in Time
Adjusting the Map Display in Power BI Desktop
- Filled Maps
- Tree Maps
- Gauges
- Additional Visuals
- Loading Custom Visuals
A Rapid Overview of a Selection of Custom Visuals

Module 10 - Text-Based Visualizations

Power BI Desktop Dashboards
- Tables
  - Creating a Basic Table
  - Deleting a Table
  - Copying a Table
  - Changing the Table Size and Position
  - Changing Column Order
  - Removing Columns from a Table
- Types of Data
  - Enhancing Tables
  - Table Granularity
  - Creating a Matrix
  - Multi-Row Cards
  - Switching Between Table Types

Module 11 - Charts in Power BI Desktop

A First Chart
- Basic Chart Types
  - Column Charts
  - Line Charts
  - Pie Charts
- Essential Chart Adjustments
- Donut Charts
- Funnel Charts
- Multiple Data Values in Charts
- 100% Stacked Column and Bar Charts
- Other Chart Types
  - Scatter Charts
  - Bubble Charts
  - Waterfall Dual-Axis Charts
- Data Details
- Drill Down
- Enhancing Charts
- Bubble Chart Play Axis

Module 12 - Other Types of Visuals

Maps
- Using Geographical Data
- Dynamic Filtering
Data Visualization and Power of R in Power BI
RLS (Row Level Security)
Publishing Reports to Power BI
Creating Power BI Templates

Module 13 - Filtering Data

Filters
- Visual-Level Filters
- Filtering Different Data Types
- Advanced Text Filters
- Specific Visualization-Level Filters
- Multiple Filters
- Page-Level Filters
- Report-Level Filters
- Removing Filters
- Using the Filter Hierarchy
- Filtering Tips

Module 14 - Using Slicers

Slicers
- Formatting Slicers
- Using Charts as Slicers
  - Charts as Filters
  - Highlighting Chart Data
  - Cross-Chart Highlighting
  - Highlighting Data in Bubble Charts
  - Column and Bar Charts as Filters
- Specifying Visual Interactions
- Choosing the Correct Approach to Interactive Data Selection

Module 15 - Enhancing Dashboards

Adding Text Boxes to Annotate a Report
Modifying the Page Background Colour
Anomaly Detection
Smart Detection
Gen AI Functionalities
Images
Adding Shapes
Organizing Visuals on the Page
Exporting Data from a Visualization

Get Expert Career Guidance

Not sure which course is right for you? Book a free consultation with one of our career strategists! Our experts are here to help you explore your options, answer your questions, and guide you toward the best path to achieve your career goals.

Book A Meeting

Course Outcomes

Master Data Visualization: Understand key principles for effective data visualization.
Tableau Proficiency: Use Tableau to connect data sources and create interactive dashboards.
Enhance Analytical Skills: Analyze data trends and patterns through various chart types.
Implement Geospatial Analytics: Apply geospatial techniques for enriched data insights.

