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Cleaning Dirty Data with Power Query in Power BI
Rating: 4.4 out of 5(3 ratings)
172 students

Cleaning Dirty Data with Power Query in Power BI

Master Data Cleaning with Power Query with 6 very dirty data case-studies in 45 Minutes
Created byAhmed Oyelowo, Foresight BI & Analytics. 100,000+ Learners
Last updated 3/2026
English
English [Auto],

What you'll learn

  • Through 6 different very dirty datasets, you will master Data Cleaning with Power Query
  • You will understand the analytical thinking that makes you become excellent at data cleaning
  • You will learn different Power Query techniques for cleaning dirty data
  • You will also learn how to leverage and combine Power Query user interface buttons to complete data cleaning tasks

Course content

1 section • 7 lectures • 46m total length

  • Introduction to Data Cleaning1:47

    Identify what’s wrong in dirty data using Power Query, because 80% of cleaning is recognizing issues, then apply the right Power Query tools to fix irregularities.

  • Data Cleaning Example 18:00

    Identify and fix badly structured sales data by transforming rows into columns, unpivoting dates, and applying correct data types in Power Query for Power BI.

  • Data Cleaning Example 26:13

    Learn a practical Power Query workflow to clean badly structured sales data in Power BI by transposing, merging columns, unpivoting, splitting by a delimiter, and finalizing data types and headers.

  • Data Cleaning Example 36:07

    Clean jumbled customer data in Power Query by extracting names, addresses, ages, and genders between delimiters, renaming columns, and converting to proper data types for Power BI analysis.

  • Data Cleaning Example 44:34

    This example shows how to split a mixed quantity column into a numeric quantity and unit text using Power Query in Power BI, with column from example.

  • Data Cleaning Example 57:41

    Clean dirty data by transforming merged product and amount cells into a proper table using split by delimiter, duplicate queries, and merge operations in Power Query for Power BI.

  • Data Cleaning Example 611:46

    Clean a dirty Power Query dataset by splitting a single hierarchical column into account type, category, and subcategory using delimiters and fill-down.

Requirements

  • No programming experience required
  • A Windows computer with minimum of Windows 10 OS
  • Power BI Desktop Installation
  • You should have an open mind to learning
  • Basic knowledge of Power BI and Power Query

Description

Cleaning Dirty Data with Power Query in Power BI is a must-know skill for any Power BI Analyst. It is known that Data Analysts and Data Scientists spend 60 to 80% of their time cleaning and preparing data. Although, most people are not conscious of the reason why this is the case. Simply put, the success of the entire flow of data from it's raw for to insights depend on how good the data is.

In this Cleaning Dirty Data with Power Query in Power BI Course, 5x Microsoft MVP and Microsoft Certified Trainer, Ahmed Oyelowo will teach you the psychology and the technical aspects of cleaning dirty data, using Power Query in Microsoft Power BI.

Why should you take this course?


  1. Explanations to complex dirty data are simplified

  2. You will not only learn the steps and the How, you will understand the Why

  3. There are 6 different dirty data of the dirtiest data you can find anywhere that is treated in the course. This will let you understand the approach of thinking to solve dirty data problems

  4. All the dirty data cleaning insights is packed in about 45 minutes of instructions

  5. You will have the dirty data files, so you can use to practice and include in your project portfolios

Who this course is for:

  • Power BI Beginners
  • Anyone preparing for Power BI Certification
  • Data Analysts and Data Scientists
  • Aspiring Data Analysts and Data Scientists
  • Working Professionals
  • Excel users
  • Tableau users
  • Qlik users
  • R and Python programmers
  • SQL and Database Professionals
  • Intermediate Power BI users

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