Coursera

Data Engineering in AWS (Coursera)

Offered by Whizlabs,

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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
Course 1 of 5 in the Exam Prep MLS-C01: AWS Certified Specialty Machine Learning Specialization.

What You Will Learn

Analyze various data gathering techniques
Analyze techniques to handle missing data
Implement feature extraction and feature selection with Principal Component Analysis and Variance Thresholds

Syllabus

WEEK 1
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.

WEEK 2
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.

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Coursera

Whizlabs

Infrastructure Automation with Terraform (Coursera)

CS: Information & Technology

Terraform is one of the most recognized infrastructure automation tools that help in addressing various real-life challenges. Infrastructure automation is a reliable instrument for improving productivity, efficiency, and reduction of operational downtime for enterprises. As a result, you can gain better career opportunities with skills in infrastructure automation with Terraform.

Aug 17th 2026

5-12 Weeks

GCP Azure Google Cloud Platform

Coursera

Google Cloud

Preparing for the Google Cloud Professional Data Engineer Exam (Coursera)

CS: Information & Technology Computer Science

From the course: "The best way to prepare for the exam is to be competent in the skills required of the job." This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job.

Aug 24th 2026

5-12 Weeks

Machine Learning Data Engineer Google Cloud

Coursera

Duke University

Introduction to Machine Learning (Coursera)

Data Science

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Aug 24th 2026

5-12 Weeks

ML NLP Machine Learning

Coursera

University of Illinois at Urbana-Champaign

Machine Learning for Accounting with Python (Coursera)

Data Science

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.

Aug 24th 2026

5-12 Weeks

Algorithms Machine Learning Clustering

Coursera

University of Michigan

Applied Machine Learning in Python (Coursera)

Statistics & Data Analysis Data Science

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial.

Aug 17th 2026

4 Weeks

Python ML Machine Learning

Coursera

LearnQuest

Developing AI Applications on Azure (Coursera)

Data Science

This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning.

Aug 24th 2026

5-12 Weeks

ML Artificial Intelligence Machine Learning

Coursera

Universidad Nacional de Colombia

Sistemas difusos (Coursera)

Statistics & Data Analysis Data Science

Los sistemas difusos permiten efectuar cálculos cuando hay información con incertidumbre, o cuando se debe combinar información tanto cuantitativa como cualitativa. Se trata de una aproximación matemática para modelar esas situaciones. Este curso está diseñado para ayudar a entender y explicar cómo funcionan dichos sistemas. El curso tiene una aproximación teórica y práctica. Los principios matemáticos son de un nivel bajo y están al alcance de un público muy amplio. El curso cuenta con varios laboratorios para aprender a utilizar las herramientas de software que usan esos principios. Este componente práctico requiere una comprensión mínima de programación.

Aug 24th 2026

4 Weeks

Machine Learning Coursera Plus Fuzzy Systems

Coursera

Stanford University

Probabilistic Graphical Models 2: Inference (Coursera)

Statistics & Data Analysis Data Science

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.

Aug 17th 2026

5-12 Weeks

Machine Learning PGM Inference

Coursera

Politecnico di Milano,EIT Digital

Data Science for Business Innovation (Coursera)

Statistics & Data Analysis Data Science

The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.

Aug 24th 2026

4 Weeks

NoSQL Machine Learning Big Data

Coursera

AWS

Getting Started with Data Analytics on AWS (Coursera)

Computer Science

With the explosion of data collection enabled by the internet, mobile applications and transformation into the cloud, effective data analytics is turning into a critical tool in practically every domain – from academia to enterprise. In this course, learners will get an introduction to the different types of data analytics (descriptive, diagnostic, predictive and prescriptive) and dive into the basics of descriptive data analysis using data and tools in the AWS Cloud.

Aug 24th 2026

1 Week

Amazon Data Analytics AWS

Coursera

New York University Tandon School of Engineering

Fundamentals of Machine Learning in Finance (Coursera)

Data Science

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.

Aug 17th 2026

4 Weeks

Finance Machine Learning Clustering

Coursera

Google Cloud

Introduction to Image Generation (Coursera)

CS: Information & Technology

This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

Aug 17th 2026

3 Weeks

Machine Learning Coursera Plus Image Generation