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Data Cleaning in Darija: Step by Step
Rating: 4.8 out of 5(8 ratings)
321 students

Data Cleaning in Darija: Step by Step

Learn how to clean messy real-world data using Python: handle NaNs, outliers, and inconsistencies
Created byAbdellatif Satir
Last updated 4/2026
Arabic

What you'll learn

  • Clean messy datasets using Python and Pandas
  • Detect data problems using Exploratory Data Analysis (EDA)
  • Handle missing values (NaNs) using different techniques
  • Fix inconsistent and incorrect data formats
  • Identify and treat outliers in datasets

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Course content

5 sections • 25 lectures • 1h 14m total length

  • 1-What is Data Cleaning & Why is it Important?4:12
  • 2-Types of Dirty Data: NaNs, Outliers, Inconsistencies & Duplicates2:58
  • 3-Messy Data vs Clean Data2:36
  • 4-How AI Learns from Data2:33
  • 5-AI Project Lifecycle: Why Data Cleaning Takes 80%1:31

Requirements

  • Basic Python and Pandas knowledge
  • A computer with Python installed (Anaconda or Jupyter Notebook recommended)
  • No prior experience in data cleaning required

Description

Race Description

Data in the real world is messy.

Missing values, inconsistent formats, duplicate entries, and outliers can completely break your analysis or machine learning models. That's why data cleaning is one of the most important skills in data science.

In this course, you will learn how to clean and prepare real-world datasets step by step, using Python and practical techniques.

What makes this unique course is that it is explained in Darija, making complex data science concepts simple and accessible for Arabic speakers.


What You'll Learn

  • How to explore datasets using EDA (Exploratory Data Analysis)

  • How to detect errors and inconsistencies in data

  • How to handle missing values (NaNs) effectively

  • How to clean and standardize messy data

  • How to detect and treat outliers

  • How to prepare datasets for machine learning


Why This Course?

Most courses focus only on models... but in reality:
80% of a data scientist's work is data cleaning

This course focuses on the real skills you actually need to work with data.

You will not just learn theory — you will work on practical examples and real datasets.


Tools You'll Use

  • Python

  • Pandas

  • NumPy

  • Matplotlib


By the End of This Course

You will be able to take any messy dataset and transform it into a clean, structured dataset ready for analysis or machine learning.

Who this course is for:

  • Beginners in data science and Python
  • Students who want to learn practical data cleaning skills
  • Anyone working with messy datasets
  • Arabic/Darija speakers interested in AI and data analysis

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