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:

1. Introduction to Advanced Power BI Course

   • Introduction to the trainer

   • Overview of the course

   • Common challenges in mastering Power BI

   • Importance of core concepts

2. 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

3. Data Cycle: Data Transformation

   • Importance of data transformation

   • Common data issues

   • Automating data transformation

   • Data wrangling and munging

4. Data Cycle: Data Consolidation

   • Importance of data consolidation

   • Data flattening vs. data modeling

   • Benefits of data modeling

   • Handling large datasets

5. Data Cycle: Enrichment, Visualization & Sharing

   • Data enrichment techniques

   • Creating compelling visualizations

   • Effective data sharing methods

6. Data Transformation: Finding Problems & Understanding Column Profile

   • Identifying data problems

   • Understanding column profiles

   • Using data profiling tools

7. Data Transformation: Fuzzy Match

   • Concept of fuzzy matching

   • Implementing fuzzy matching in Power BI

   • Handling data quality issues

8. Data Transformation: Transformation Table with Fuzzy Match

   • Creating transformation tables

   • Using transformation tables with fuzzy matching

   • Best practices for accurate data mapping

9. Data Transformation: Fuzzy, Transformation Table Practice

   • Hands-on practice with transformation tables

   • Troubleshooting common problems

   • Performing sense checks

10. Data Transformation: Transforming City Data Set

    • Case study: transforming city data

    • Applying learned techniques

    • Reinforcing key concepts through practical application

11. Data Transformation: Completing Sales File

    • Cleaning and transforming sales data

    • Handling errors and missing values

    • Making executive decisions on data handling

12. Data Transformation: Product File

    • Importing and cleaning product data

    • Standardizing product information

    • Dealing with inconsistent data entries

13. Data Consolidation: Model Formatting

    • Understanding automatic relationship detection

    • Deactivating auto-detect for manual relationship management

    • Formatting and enriching data

14. Data Enrichment: Calendar Table (Simple)

    • Creating a simple calendar table

    • Using DAX for date-related calculations

    • Enhancing reports with date intelligence

15. Data Enrichment: Calendar Table (Fiscal Year)

    • Creating a fiscal year calendar table

    • Customizing date intelligence for fiscal reporting

    • Utilizing DAX for advanced date calculations

16. Q&A Session

    • Recap of previous sessions

    • Addressing participant questions and concerns

    • Practical tips and insights from real-world use cases

17. Data Model: Fact Table

    • Understanding fact tables

    • Characteristics and purpose of fact tables

    • Creating and managing fact tables in Power BI

18. Data Model: Dimension Table & Star Schema

    • Understanding dimension tables

    • Characteristics and purpose of dimension tables

    • Implementing star schema in data modeling

19. Data Model: Cardinality and Cross Filter Direction

    • Understanding cardinality in relationships

    • Managing cross-filter direction

    • Best practices for relationship management

20. Data Model: Merge and Role-Playing Dimensions

    • Merging tables for optimized data models

    • Creating role-playing dimensions

    • Advanced data modeling techniques

21. Data Model: Comparing 2 Fact Tables (Theory)

    • Theoretical concepts of comparing fact tables

    • Understanding common grains

    • Implications of comparing different grains

22. Data Model: Comparing 2 Fact Tables (Practice)

    • Practical application of comparing fact tables

    • Handling many-to-many relationships

    • Best practices for accurate comparisons

23. Comparing Sales and Inventory (Considerations & Reporting)

    • Comparing sales and inventory data

    • Managing data discrepancies

    • Effective reporting techniques

24. 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

25. Comparing Order Date and Ship Date

    • Comparing different date fields

    • Handling date discrepancies

    • Creating meaningful insights from date comparisons

26. Comparing Target Sales vs Actual Sales Part 1

    • Introduction to target vs actual sales comparison

    • Setting up the data model

    • Creating relationships and calculations

27. 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

28. Role Level Security

    • Implementing role-level security in Power BI

    • Managing user access and permissions

    • Best practices for secure data models

29. Normalizing a Flat File

    • Introduction to normalizing flat files

    • Step-by-step process for creating dimension tables

    • Best practices for efficient data modeling

30. Closing and Q&A

    • Recap of the entire course

    • Final questions and answers