Data Engineering in AWS

Data Engineering in AWS is the first course in the AWS Certified Machine Learning Specialty specialization. This course helps learners to analyze various data gathering techniques. They will also gain insight to handle missing data. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:30-3:00 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners. Module 1: Introduction to Data Engineering Module 2: Feature extraction and feature selection Candidate should have at least two years of hands-on experience architecting, and running ML workloads in the AWS Cloud. One should have basic ML algorithms knowledge. By the end of this course, a learner will be able to: - Understand various data-gathering techniques - Analyze techniques to handle missing data - Implement feature extraction and feature selection with Principal Component Analysis and Variance Thresholds.

• Introduction to Data Engineering
• Welcome to Week 1 of Data Engineering in AWS Course. This week will begin with understanding SageMaker Jupyter Notebooks setup. We’ll also get an overview of handling and dropping Missing Data.This week will end by analyzing information about Gathering data.
• Feature extraction and feature selection
• Welcome to Week 2 of Data Engineering in AWS Course. This week , we’ll learn to perform Feature extraction and feature selection with Principal Component Analysis and Variance Thresholds. We’ll also explore feature extraction and feature selection techniques. By the end of this week, we’ll analyze AWS Migration services and tools.