Data Engineering Overview

This video tutorial covers three main parts of data engineering: gathering and cleansing data, feature engineering and handling imbalanced data, and deploying machine learning models. It explains how to consolidate data from various sources, calculate statistical summaries, visualize data, and cleanse it by removing outliers. The tutorial also discusses feature engineering, scaling data, and splitting datasets for training, validation, and testing. Finally, it covers deploying machine learning models in server infrastructure for applications.

What methods can be used for scaling data, and why is it necessary?

How is data split for training, validation, and testing, and why is this process important?

What are the key considerations when deploying machine learning models?