Introduction to Data Cleaning with Python
This course introduces fundamental concepts of data cleaning using Python, covering essential libraries, handling missing values, detecting and removing duplicates, dealing with outliers, and normalizing data for analysis.
Cleaning and Transforming Data with Pandas
This course explores advanced Pandas functionalities for transforming data, handling categorical and text data, processing date-time values, and performing feature engineering for better analysis.
Data Cleaning and Validation for Machine Learning
This course ensures data integrity, feature selection, anomaly detection, and validation for ML models. The goal is to remove noisy, inconsistent, or biased data before training.
Automating Data Cleaning with Python
This course focuses on automating the data cleaning process using Python. It covers pipeline creation, automation with functions, handling large datasets efficiently, and logging and debugging.
Advanced Data Cleaning: Handling Text Data
This course extends data cleaning techniques to handle text-based data in tabular datasets. It covers cleaning and processing text columns, dealing with mixed data types, extracting meaningful features from text, and preparing text data for machine learning.
