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Master Data Cleaning: Python, Excel & Power Query
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Master Data Cleaning: Python, Excel & Power Query

About this course

Master Data Cleaning: Python, Excel & Power QueryThe Complete Guide to Cleaning, Transforming, and Preparing Real-World Datasets for AnalysisAre you tired of spending hours cleaning messy spreadsheets or trying to make sense of inconsistent data? Do you want to master the essential data wrangling skills that professionals use every day to turn chaotic raw data into clean, structured datasets ready for analysis?You’ve found the right course.Whether you're a beginner, a data enthusiast, or a working professional looking to improve your data handling skills, this course will teach you how to clean and transform real-world data using Microsoft Excel, Power Query, and Python (pandas) — all with hands-on projects and real business scenarios.What This Course Teaches YouData cleaning is not glamorous, but it's one of the most critical steps in the data lifecycle. Without clean data, your dashboards, reports, and machine learning models will all suffer.This course helps you develop a toolbox of techniques to clean, validate, merge, and prepare datasets — no matter where the data comes from.By the end of the course, you’ll confidently handle:Duplicate entriesInconsistent text formatsMissing or blank fieldsMerging messy datasetsReconciliations (like sales vs. inventory)Transforming dirty Excel or CSV files into clean data tablesWe’ll show you how to solve these problems using:Excel formulas like IF(), TRIM(), TEXT(), and VLOOKUP()Power Query for automated and repeatable transformationsPython’s pandas library for efficient, code-based data cleaning Tools CoveredI focus on the three most widely used data cleaning tools:Microsoft Excel<

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62/100

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38/45
Who stands behind it
8/35
How complete the listing is
16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

  • apply Excel formulas for data cleaning
  • utilize Power Query for automated data transformations
  • use Python's pandas library for data manipulation
  • identify and handle duplicate entries
  • standardize inconsistent text formats
  • fill in missing or blank fields
  • merge multiple datasets
  • perform data reconciliations
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