Data Cleaning in Darija: Step by Step
| Development | |
| 157 students | |
| 1 lectures | |
| 2026-05-26 07:59:21 | |
| $0 $0 |
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.