Summary  
This chapter covers techniques for detecting and imputing missing values in datasets by using statistical methods (mean, median, mode) and forward/backward filling to maintain data consistency.

General domain of usage  
Data preprocessing for analytics

Learn how to **handle missing data** in a dataset using Power Query. Missing values can cause **incomplete** or **inaccurate** analysis, making it essential to address them effectively. You will explore how to identify these gaps and replace them with appropriate values using techniques such as **mean**, **median**, or **mode** imputation. Additionally, you will discover how to apply **forward-fill** and **backward-fill** methods to maintain data consistency.

What is the primary reason for handling missing data in Power BI?


What method can you use to replace missing values in Power Query?

Power BI is an essential tool for any aspiring data analyst or business professional. Learn how to transform raw data into insightful visualizations and interactive dashboards, connect to various data sources, and create compelling reports for informed decision-making.

Dive into the world of Power BI and build a solid foundation. You'll cover everything from installation and setup to connecting data sources and creating your first report.

Explore essential data preparation techniques, including cleaning, transforming, and combining multiple data sources. You'll also tackle handling missing values, data profiling, and various data manipulation methods.


Master the fundamentals of data modeling, from understanding data models and creating relationships to getting the hang of DAX basics. You'll also learn to create measures and calculated columns.


Discover various visualization techniques and customize visuals for better insights. You'll also explore advanced features like slicers, filters, and drill-down/drill-through capabilities to enhance your data analysis.


Boost the usability and storytelling power of your reports by using themes, configuring report pages, and leveraging advanced features like the Analyze feature and identifying outliers. You'll also create groups, bins, and clusters for deeper insights.


Handling Missing Values

1. What is the primary reason for handling missing data in Power BI?

2. What method can you use to replace missing values in Power Query?

Awesome!

Handling Missing Values

1. What is the primary reason for handling missing data in Power BI?

2. What method can you use to replace missing values in Power Query?