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 is the primary goal of data visualization in data engineering?

• To remove outliers from the dataset

• To create complex models

• To graphically represent data for initial analysis

• To store data efficiently

Which of the following is NOT a part of data cleansing?

• Converting categorical data to numeric

• Removing outliers

• Filling missing entries

• Eliminating corrupted information

What is the purpose of feature engineering in machine learning?

• To create and select input descriptors

• To cleanse data

• To visualize data

• To deploy models

How can imbalanced data be addressed in data engineering?

• By converting all data to categorical form

• By scaling all data to a range of 0 to 1

• By oversampling or undersampling certain classes

• By ignoring the imbalance

Why is data scaling important in machine learning?

• To deploy models faster

• To visualize data better

• To cleanse data more effectively

• To improve the training process

What is the role of a confusion matrix in data engineering?

• To scale data

• To cleanse data

• To visualize data

• To represent misclassification errors

What is a key aspect of deploying machine learning models?

• Performing feature engineering

• Cleansing data

• Setting up scalable server infrastructure

• Creating complex visualizations