Data Cleaning with Python: From Raw to Ready Data

Clean real-world datasets in Python using pandas step by step

New

Created byMousa Al-Salahi

Last updated 7/2026

English

What you'll learn

Clean and prepare raw datasets in Python using pandas.
Handle missing and null values with appropriate techniques.
Detect and remove duplicate records safely.
Clean and standardize text data (lower/upper case, spaces, categories, types).
Build a simple, reusable data cleaning workflow for future projects.

Course content

1 section • 14 lectures • 1h 54m total length

Explore & Select Data from DataFrame7:36
Filter Rows using ( Loc & iLoc )7:19
Filtering RowUsing Conditions5:52
Advanced Filtering ( Range , isin , str.contain )10:17
Finding & Handling missing Values11:48
Filling Missing Values ( Fillna )9:10
Finding & Dropping Duplicates7:40
Cleaning Strings & Text Columns7:20
Feature Engineering using Where3:26
Feature Engineering using Bins8:28
Date Handling11:19
Sort Columns7:56
Rename Columns7:11
Group by9:10

Requirements

There are no strict requirements for this course.
Basic understanding of tables (rows and columns) from Excel or any spreadsheet.
Very basic Python knowledge (running code cells, simple variables and lists).
A computer with internet access and the ability to install Python, or use Google Colab in the browser.

Description

This course teaches you how to clean and prepare real-world data using Python and pandas in a simple, practical way. You will start from raw, messy datasets and learn step by step how to handle missing values, remove duplicates, fix inconsistent text, and correct common data quality issues. By the end of the course, you will have a reusable data cleaning workflow that you can apply to any dataset before analysis or reporting.

Lessons / sections (bullet points)

Introduction and setup: Install Python or use Google Colab, load datasets, and overview of pandas.
Data inspection and quality check: Explore data structure, types, and detect basic issues.
Handling missing and null values: Identify missing data and apply different strategies to deal with it.
Removing duplicate records: Find and safely remove duplicates based on full rows or key columns.
Removing duplicate records: Find and safely remove duplicates based on full rows or key columns.
Cleaning text and categorical data: Standardize lower/upper case, trim spaces, fix inconsistent categories.
Fixing data types and formats: Convert columns to numbers, dates, and categories correctly.
Handling dates like years months days and seasons by extracting part of dates
Handling and cleaning columns names
Prepare cleaned data for analysis in the upcoming steps

Who this course is for:

Beginners in data analysis who work with messy Excel/CSV files.
Junior data analysts who want to clean data using Python and pandas.
Learners with basic Python skills who need to handle missing values and duplicates.
Anyone who wants a simple, practical introduction to data cleaning.