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Data Cleaning in Python: From Messy Data to Clean Data, Learn how to clean messy real-world data using Python: handle NaNs, outliers, duplicates and inconsistencies.
Description
Course 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.
By the end of this course, you will be able to confidently clean any dataset and prepare it for Data Science or Machine Learning projects.
What you will learn
- How to detect and analyze data quality issues using EDA
- How to handle missing values in numerical and categorical data
- How to clean inconsistent and messy datasets
- How to detect and remove duplicate records
- How to detect and handle outliers using multiple methods
- How to prepare clean datasets ready 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 or Data Analysis
- Students who want to learn how to clean real-world datasets
- Aspiring Data Analysts and Machine Learning practitioners
- Python developers who want to improve their Pandas skills
- Anyone who struggles with messy or incomplete data
