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Master Data Cleaning: Python, Excel & Power Query
Rating: 4.0 out of 5(12 ratings)
21 students

Master Data Cleaning: Python, Excel & Power Query

Master practical data cleaning skills with Excel formulas, Power Query automation, and Python scripts.
Created byRavi Singh
Last updated 9/2025
English

What you'll learn

  • Identify and fix messy, inconsistent, incomplete, and duplicate data across various sources.
  • Use functions like IF(), TRIM(), TEXT(), VLOOKUP(), and DATA VALIDATION to clean and format spreadsheets effectively.
  • Import messy files, transform column headers, remove blanks, filter rows, and automate repetitive cleaning using Power Query.
  • Use Python’s pandas library to load, clean, merge, and transform real-world datasets using functions like .dropna(), .fillna(), .str.replace(), and .groupby().
  • Standardize inconsistent date formats, correct column naming issues, and unify naming conventions using both Excel and code.
  • Identify and remove exact and near-duplicate records using Excel tools, Power Query logic, and pandas .duplicated() methods.
  • Build workflows that allow you to clean new data in one click using Power Query and reusable Python scripts.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

See a demo
Image of coding exercise example

Course content

10 sections19 lectures1h 19m total length
  • Become a Data Wrangler - Introduction3:41
  • About The Author1:40
  • Why Data Wrangling Matters?2:36
  • The Tools You’ll Learn in This Course1:00
  • Data Wrangling - Course Introduction0:53

Requirements

  • Ability to navigate files, folders, and applications confidently.
  • Basic understanding of Excel spreadsheets
  • Experience with formulas like SUM, IF, and VLOOKUP (helpful but not required)
  • Understanding of basic Python syntax (print(), variables, lists/dictionaries)
  • No advanced coding required — Python basics will be reinforced in the course
  • Willingness to try tools like Power Query and Python/pandas even if you're new to them.
  • Power Query is built-in from Excel 2016+
  • Python (v3.7 or later) Installed via Anaconda or Python org
  • Jupyter makes it easy to follow step-by-step
  • VS Code or any other IDE is also fine
  • Internet Connection Required to download data files, Python packages, or follow along with GitHub-hosted resources.

Description

Master Data Cleaning: Python, Excel & Power Query

The Complete Guide to Cleaning, Transforming, and Preparing Real-World Datasets for Analysis

Are 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 You

Data 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 entries

  • Inconsistent text formats

  • Missing or blank fields

  • Merging messy datasets

  • Reconciliations (like sales vs. inventory)

  • Transforming dirty Excel or CSV files into clean data tables

We’ll show you how to solve these problems using:

  • Excel formulas like IF(), TRIM(), TEXT(), and VLOOKUP()

  • Power Query for automated and repeatable transformations

  • Python’s pandas library for efficient, code-based data cleaning

Tools Covered

I focus on the three most widely used data cleaning tools:

  1. Microsoft Excel
    Great for ad-hoc cleaning and understanding patterns quickly. You’ll learn how to:

    • Use formulas for detecting and fixing issues

    • Apply data validation

    • Use PivotTables for quick aggregations

  2. Power Query (Excel/Power BI)
    Ideal for automating the cleanup process. You'll learn to:

    • Import and transform messy files

    • Normalize headers, split/merge columns

    • Remove blanks and errors with one-click transformations

  3. Python (pandas)
    The industry-standard tool for scalable data cleaning. You’ll learn:

    • How to load and inspect messy datasets

    • Use dropna(), fillna(), replace(), str.lower(), and more

    • Merge datasets and handle duplicates with ease

No prior coding experience is required — we guide you step-by-step.

Who This Course Is For

This course is designed for a wide range of learners, including:

  • Data analysts and business analysts who want to clean data faster and more reliably.

  • Students and career switchers who want to build a data portfolio and gain practical skills.

  • Excel users and non-programmers looking to upgrade to more powerful tools like Power Query and pandas.

  • Consultants and freelancers who deal with messy client data and need repeatable workflows.

  • Professionals in operations, finance, HR, sales, and marketing who deal with spreadsheets and CSVs every day.

What You’ll Learn (By Section)

  1. Data Cleaning Basics

    • What makes data messy

    • Common formats, missing values, and inconsistencies

  2. Excel for Data Cleaning

    • Cleaning with formulas: IF, TEXT, VLOOKUP, TRIM

    • Using filters, validation, and conditional formatting

  3. Power Query for Automation

    • Loading data from folders and files

    • Splitting, merging, and unpivoting columns

    • Removing duplicates and fixing types

  4. Python & pandas for Real-World Cleaning

    • Cleaning text columns

    • Removing duplicates

    • Dealing with nulls and formatting issues

    • Merging datasets with .merge() and .concat()

  5. Capstone Projects

    • Clean messy HR datasets with inconsistent employee names and IDs

    • Reconcile sales vs. inventory using merges, grouping, and filters

    • Transform multiple Excel files into a unified clean dataset

Each project mimics a real job task you’ll face in the field — perfect for practice and your portfolio.

What You’ll Achieve

By the end of the course, you'll be able to:

  • Confidently clean and prepare messy Excel/CSV data using industry tools

  • Automate data cleaning workflows using Power Query and pandas

  • Merge multiple datasets with inconsistent IDs or formatting

  • Spot and resolve real-world issues like duplicates, bad formatting, and null values

  • Build reusable scripts and queries for repeatable processes

  • Present clean, analysis-ready data for reporting or machine learning

What’s Included

  • Step-by-step lessons with examples

  • 4+ real-world datasets to practice on

Why This Course Is Different

This isn’t just theory — it’s hands-on learning from the ground up. I don’t just show you tools; we show you how to use them in the messy, imperfect world of real business data.

Each section ends with practical challenges and mini-projects to reinforce your skills. You’ll walk away not just knowing what to do, but why it works.

Ready to Master Data Cleaning?

Whether you're building dashboards, preparing reports, or feeding a data pipeline — clean data is your foundation.

Enroll now and start cleaning smarter — not harder.
Learn Excel, Power Query, and Python the practical way.

Who this course is for:

  • Aspiring Data Analysts & Data Scientists Looking to build a strong foundation in data cleaning — the most critical and time-consuming step in any data project.
  • Business Analysts & Excel Users Who want to go beyond spreadsheets and learn how to automate and scale data cleaning using Power Query and Python.
  • Students & Recent Graduates Preparing for roles in data, analytics, or reporting and looking to gain practical, hands-on skills with real-world datasets.
  • Professionals Working with Messy Data In finance, HR, sales, marketing, or operations who regularly receive inconsistent or incomplete data from different sources.
  • Anyone Transitioning to a Data Role Who may not have a programming background but wants to quickly upskill in essential data wrangling techniques using modern tools.
  • Freelancers and Consultants Who clean and combine data from multiple clients and need reliable, repeatable workflows.