Data Cleaning and Preprocessing Techniques

Intro to Data Analysis with PythonData Cleaning and Preprocessing Techniques

Embarking on this course allows you to deeply understand and apply data cleaning and preprocessing techniques. It systematically covers the concepts of data cleaning, handling missing values, normalization, binning, encoding, and more, aiming to equip you with practical skills for preparing data for analysis or machine learning tasks.

Lessons and practices

Lesson 1: Understanding and Handling Missing Values in Datasets with Python

Applying Missing Values Handling to Clients' Personal Information Dataset

Replacing Mean with Median

Filling the Missing Values in the Clients Dataset

Addressing Missing Values in Client Data

Lesson 2: Handling Duplicates and Outliers in Datasets

Identifying Duplicates and Outliers in Height Dataset

Cleaning Duplicates from the Dataset

Cleaning Up School Data: Handling Duplicates and Outliers

Removing Duplicates and Handling Outliers in Student Data

Clean School Data: Handling Duplicates and Outliers