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.