Data Cleaning With Polars

Description

80% of data science work is data cleaning. Building a machine learning model using unclean or messy data can lead to inaccuracies in your model performance. Therefore, it is important for you to know how to clean various real-world datasets. If you're looking to enhance your skills in data manipulation and cleaning, this course will arm you with the essential skills needed to make that possible. This course is carefully crafted to provide you with a deeper understanding of data cleaning using Polars, a new blazingly fast DataFrame library for Python that enables you to handle large datasets with ease.

Five Different Datasets

All clean datasets are the same, but every unclean dataset is messy in its own way. This course includes five unique datasets and gives you a walkthrough of how to clean each one of them

Data Transformation

Data cleaning is about transforming the data from changing data types to removing unnecessary columns or rows. It’s also about dropping or replacing missing values as well as handling outliers. You will learn how to do all that in this course.

Ready-to-Use Skills

The lectures in this course are designed to help you conquer essential data cleaning tasks. You'll gain job-ready skills and knowledge on how to clean any type of dataset and make it ready for model building.