This Advanced Power BI course is meticulously designed to equip professionals with the expertise needed to master data analytics and visualization at an advanced level. By delving into critical aspects such as data transformation, modeling, and visualization, this course ensures you gain comprehensive skills to handle complex data scenarios effectively. Participants will learn to connect and consolidate data from diverse sources, automate data processes, and build robust data models. The course also covers advanced topics like role-level security, fuzzy matching, and the creation of transformation tables, enabling you to manage and protect data with confidence.

Taking this course will provide you with practical, hands-on experience through real-world applications and case studies. You will learn to create insightful reports and compelling visualizations that drive informed decision-making. By the end of the course, you will be equipped not only with advanced technical skills but also with the ability to apply these techniques to solve business problems and optimize data-driven strategies. This course is ideal for professionals looking to elevate their Power BI capabilities and leverage data analytics to achieve business success.

Course Outline:

Introduction to Advanced Power BI Course

• Introduction to the trainer

• Overview of the course

• Common challenges in mastering Power BI

• Importance of core concepts

Introduction to the trainer

Overview of the course

Common challenges in mastering Power BI

Importance of core concepts

Data Cycle: Getting Data

• Starting with a vision and end goals

• Identifying data sources

• Connecting to disparate systems

• Centralized data warehouses

• Methods for importing data

Starting with a vision and end goals

Identifying data sources

Connecting to disparate systems

Centralized data warehouses

Methods for importing data

Data Cycle: Data Transformation

• Importance of data transformation

• Common data issues

• Automating data transformation

• Data wrangling and munging

Importance of data transformation

Common data issues

Automating data transformation

Data wrangling and munging

Data Cycle: Data Consolidation

• Importance of data consolidation

• Data flattening vs. data modeling

• Benefits of data modeling

• Handling large datasets

Importance of data consolidation

Data flattening vs. data modeling

Benefits of data modeling

Handling large datasets

Data Cycle: Enrichment, Visualization & Sharing

• Data enrichment techniques

• Creating compelling visualizations

• Effective data sharing methods

Data enrichment techniques

Creating compelling visualizations

Effective data sharing methods

Data Transformation: Finding Problems & Understanding Column Profile

• Identifying data problems

• Understanding column profiles

• Using data profiling tools

Identifying data problems

Understanding column profiles

Using data profiling tools

Data Transformation: Fuzzy Match

• Concept of fuzzy matching

• Implementing fuzzy matching in Power BI

• Handling data quality issues

Concept of fuzzy matching

Implementing fuzzy matching in Power BI

Handling data quality issues

Data Transformation: Transformation Table with Fuzzy Match

• Creating transformation tables

• Using transformation tables with fuzzy matching

• Best practices for accurate data mapping

Creating transformation tables

Using transformation tables with fuzzy matching

Best practices for accurate data mapping

Data Transformation: Fuzzy, Transformation Table Practice

• Hands-on practice with transformation tables

• Troubleshooting common problems

• Performing sense checks

Hands-on practice with transformation tables

Troubleshooting common problems

Performing sense checks

Data Transformation: Transforming City Data Set

• Case study: transforming city data

• Applying learned techniques

• Reinforcing key concepts through practical application

Case study: transforming city data

Applying learned techniques

Reinforcing key concepts through practical application

Data Transformation: Completing Sales File

• Cleaning and transforming sales data

• Handling errors and missing values

• Making executive decisions on data handling

Cleaning and transforming sales data

Handling errors and missing values

Making executive decisions on data handling

Data Transformation: Product File

• Importing and cleaning product data

• Standardizing product information

• Dealing with inconsistent data entries

Importing and cleaning product data

Standardizing product information

Dealing with inconsistent data entries

Data Consolidation: Model Formatting

• Understanding automatic relationship detection

• Deactivating auto-detect for manual relationship management

• Formatting and enriching data

Understanding automatic relationship detection

Deactivating auto-detect for manual relationship management

Formatting and enriching data

Data Enrichment: Calendar Table (Simple)

• Creating a simple calendar table

• Using DAX for date-related calculations

• Enhancing reports with date intelligence

Creating a simple calendar table

Using DAX for date-related calculations

Enhancing reports with date intelligence

Data Enrichment: Calendar Table (Fiscal Year)

• Creating a fiscal year calendar table

• Customizing date intelligence for fiscal reporting

• Utilizing DAX for advanced date calculations

Creating a fiscal year calendar table

Customizing date intelligence for fiscal reporting

Utilizing DAX for advanced date calculations

Q&A Session

• Recap of previous sessions

• Addressing participant questions and concerns

• Practical tips and insights from real-world use cases

Recap of previous sessions

Addressing participant questions and concerns

Practical tips and insights from real-world use cases

Data Model: Fact Table

• Understanding fact tables

• Characteristics and purpose of fact tables

• Creating and managing fact tables in Power BI

Understanding fact tables

Characteristics and purpose of fact tables

Creating and managing fact tables in Power BI

Data Model: Dimension Table & Star Schema

• Understanding dimension tables

• Characteristics and purpose of dimension tables

• Implementing star schema in data modeling

Understanding dimension tables

Characteristics and purpose of dimension tables

Implementing star schema in data modeling

Data Model: Cardinality and Cross Filter Direction

• Understanding cardinality in relationships

• Managing cross-filter direction

• Best practices for relationship management

Understanding cardinality in relationships

Managing cross-filter direction

Best practices for relationship management

Data Model: Merge and Role-Playing Dimensions

• Merging tables for optimized data models

• Creating role-playing dimensions

• Advanced data modeling techniques

Merging tables for optimized data models

Creating role-playing dimensions

Advanced data modeling techniques

Data Model: Comparing 2 Fact Tables (Theory)

• Theoretical concepts of comparing fact tables

• Understanding common grains

• Implications of comparing different grains

Theoretical concepts of comparing fact tables

Understanding common grains

Implications of comparing different grains

Data Model: Comparing 2 Fact Tables (Practice)

• Practical application of comparing fact tables

• Handling many-to-many relationships

• Best practices for accurate comparisons

Practical application of comparing fact tables

Handling many-to-many relationships

Best practices for accurate comparisons

Comparing Sales and Inventory (Considerations & Reporting)

• Comparing sales and inventory data

• Managing data discrepancies

• Effective reporting techniques

Comparing sales and inventory data

Managing data discrepancies

Effective reporting techniques

Recap and Data Enrichment Using Custom Columns CC

• Recap of key concepts

• Data enrichment techniques using custom columns (CC)

• Practical examples and hands-on exercises

Recap of key concepts

Data enrichment techniques using custom columns (CC)

Practical examples and hands-on exercises

Comparing Order Date and Ship Date

• Comparing different date fields

• Handling date discrepancies

• Creating meaningful insights from date comparisons

Comparing different date fields

Handling date discrepancies

Creating meaningful insights from date comparisons

Comparing Target Sales vs Actual Sales Part 1

• Introduction to target vs actual sales comparison

• Setting up the data model

• Creating relationships and calculations

Introduction to target vs actual sales comparison

Setting up the data model

Creating relationships and calculations

Comparing Target Sales vs Actual Sales Part 2

• Advanced techniques for comparing target vs actual sales

• Handling complex data models

• Best practices for accurate reporting

Advanced techniques for comparing target vs actual sales

Handling complex data models

Best practices for accurate reporting

Role Level Security

• Implementing role-level security in Power BI

• Managing user access and permissions

• Best practices for secure data models

Implementing role-level security in Power BI

Managing user access and permissions

Best practices for secure data models

Normalizing a Flat File

• Introduction to normalizing flat files

• Step-by-step process for creating dimension tables

• Best practices for efficient data modeling

Introduction to normalizing flat files

Step-by-step process for creating dimension tables

Best practices for efficient data modeling

Closing and Q&A

• Recap of the entire course

• Final questions and answers

Recap of the entire course

Final questions and answers