Overview

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Data cleaning is one of the most critical stages in the analytics process, ensuring that every insight drawn from data is accurate and trustworthy. This course, Data Cleaning with Power BI, equips professionals with the skills to transform messy, inconsistent data into actionable insights using Microsoft’s most powerful business intelligence platform. Through hands-on lessons, you’ll learn how to use Power Query, DAX, and the M language to streamline your data preparation workflows. By the end of the course, you’ll be able to connect to multiple data sources, apply robust transformations, and design optimized data models ready for visualization and analysis. Unlike other Power BI courses that focus solely on dashboards, this one emphasizes the foundations of clean, structured, and meaningful data. Each module combines theory with real-world applications, enabling you to solve practical business problems using tested data-cleaning techniques. This course is ideal for data analysts, BI professionals, and data scientists who want to enhance their Power BI capabilities. While a basic understanding of BI tools is recommended, beginners can also follow along thanks to step-by-step examples and guided exercises.

Syllabus

Introduction to Power BI Data Cleaning

In this section, we explore data cleaning in Power BI, focusing on Power Query and DAX for effective data preparation and analysis.

Understanding Data Quality and Why Data Cleaning Is Important

In this section, we examine factors affecting data quality and the importance of data cleaning for accurate business intelligence. Key concepts include data integrity, best practices, and their impact on decision-making.

Data Cleaning Fundamentals and Principles

In this section, we cover data cleaning fundamentals, including quality criteria and systematic approaches for reliable analysis.

The Most Common Data Cleaning Operations

In this section, we explore common data cleaning operations in Power BI, including removing duplicates, handling missing data, and manipulating columns to improve data quality and reliability.

Importing Data into Power BI

In this section, we explore assessing data completeness, accuracy, and consistency in Power BI to evaluate data quality and ensure reliable analysis.

Cleaning Data with Query Editor

In this section, we explore data cleaning techniques in Query Editor, including pivot, merge, and type conversion, and compare its workflow with DAX for efficient data transformation.

Transforming Data with the M Language

In this section, we explore the M language for data transformation, focusing on filtering, sorting, and dynamic data sources. It emphasizes practical skills for advanced data manipulation beyond GUI tools.

Using Data Profiling for Exploratory Data Analysis (EDA)

In this section, we explore Power BI's data profiling tools for EDA, focusing on column quality, distribution, and profile to improve data reliability and analysis outcomes.

Advanced Data Cleaning Techniques

In this section, we explore advanced data cleaning techniques using Power Query, R, Python, and ML to improve data accuracy and analysis quality in Power BI.

Creating Custom Functions in Power Query

In this section, we explore creating custom Power Query functions in Power BI, focusing on planning, parameter usage, and writing M code for efficient data transformation.

Query Optimization

In this section, we explore techniques to optimize Power Query performance, including filtering data early, using native M functions, and applying lazy evaluation for efficient memory use.

Data Modeling and Managing Relationships

In this section, we explore data modeling techniques in Power BI, focusing on relationships, cardinality, and performance optimization to ensure clean and reliable data for analysis.

Preparing Data for Paginated Reporting

In this section, we explore data preparation for paginated reports in Power BI Report Builder, focusing on connecting data sources, using filters, and structuring reports for precise output.

Automating Data Cleaning Tasks with Power Automate

In this section, we explore Power Automate's role in automating data cleaning, refreshing, and notifications, enhancing data reliability and efficiency in Power BI workflows.

Making Life Easier with OpenAI

In this section, we explore how OpenAI and AI tools enhance data cleaning and preparation in Power BI, focusing on efficiency, accuracy, and workflow optimization using ChatGPT and DAX.