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Power Query | Transform & Clean Data Automatically

Power Query | Transform & Clean Data Automatically

Learn to clean, merge, shape, and automate data using Excel’s most powerful modern tool.
Created byExcelR EdTech
Last updated 11/2025
English
English

What you'll learn

  • Import data from multiple sources
  • Clean, filter, and shape data with no coding
  • Merge and append tables
  • Automate refreshes and repetitive tasks
  • Build repeatable, error-free workflows

Course content

1 section9 lectures50m total length
  • What Is Power Query?4:46

    Explore Power Query in Excel as an ETL tool that extracts data from web, pdf, csv, json, folders, and supports append and merge. Load transformed data into Excel.

  • Data Types in Power Query9:40

    Load Excel data into the Power Query editor, transform with uppercase names and date formats, create columns like hra, da, ta, and compute ctc.

  • Split Column by Delimiter1:35

    Power Query demonstrates splitting a name into first and last name by delimiter, renaming columns, and cleaning data by replacing nulls with blanks.

  • Add Column by Using Conditional Column & Date4:06

    Master adding a conditional column to prefix by gender, merge to a full name, and derive month name, day name, and age from a date column in Power Query.

  • Flash Fill in Power Query4:01

    Learn to use flash fill in Power Query by creating a column from examples, renaming it to remarks, and applying data changes, filters, and refresh to reflect source updates.

  • Edit Query, Transform in Power Query7:17

    Explore editing queries, transforming data, and grouping by region or department in Power Query to clean, shape, and load data into Excel, with auto refresh and query options.

  • Import Data From Web10:00

    Learn to import data from the web into Excel using Power Query, transform and clean tables by removing columns, changing case, splitting data, and loading results with refresh.

  • Import Data From PDF & CSV2:14

    Master Power Query to import and transform data from csv and pdf, load results into Excel, and extend to web sources with guided, hands-on steps.

  • Append & Merge Queries6:50

    Append query stacks rows from multiple Excel tables; merge query adds columns using a common department ID, then load and refresh results in Power Query.

Requirements

  • Basic Excel knowledge
  • No coding or VBA required

Description

Power Query changes the way Excel users handle data. Instead of fixing the same issues again and again—cleaning text, correcting formats, combining monthly files—you can build repeatable workflows that prepare your data automatically. It’s one of the most efficient ways to eliminate manual effort and keep your reports accurate, consistent, and ready for analysis.

Your learning begins with bringing data into Power Query. You’ll work with Excel files, CSVs, folders, and other external sources, seeing how easily the tool consolidates information from different places. Once your data is imported, you’ll explore the transformation tools that make Power Query so powerful: removing duplicates, splitting or merging columns, applying filters, replacing values, restructuring tables, and fixing data types. Every transformation is demonstrated through real situations such as cleaning attendance logs, preparing sales sheets, combining recurring monthly reports, and converting raw downloads into tidy, organised tables.

A major advantage of Power Query is automation. After you set up your steps once, they can be refreshed with a single click—instantly updating your entire report whenever new data arrives. This lets you build workflows that stay consistent and eliminate the risk of manual errors.

With these skills, you’ll be able to create clean, repeatable, and scalable data processes that save hours of work every week. Whether you’re in analytics, finance, operations, HR, or any role that depends on accurate data, Power Query will become an essential part of your toolkit.

Who this course is for:

  • Analysts, financial professionals, operations teams, data learners, and anyone working with repetitive data tasks.