We may earn an affiliate commission when you visit our partners.
Whizlabs Instructor
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/8niwqu/data
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until December 12
Annual Subscription Savings
Unlock programs from Google, Microsoft, and IBM and join 77% of learners who reported positive career outcomes.
Only $239!
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a foundational understanding of data engineering techniques in AWS, which is standard in industry
Taught by Whizlabs Instructor, who are recognized for their work in AWS data engineering
Develops skills in data gathering, missing data handling, feature extraction, and feature selection, which are core skills for data engineering in AWS
Examines techniques for handling missing data, which is a common challenge in data engineering
Explores feature extraction and feature selection techniques, which are essential for data transformation
Save this course
Save Data Engineering in AWS to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Engineering in AWS with these
activities:
Review basic statistics and linear algebra
Show steps
Reviewing basic statistics and linear algebra will help you refresh your knowledge of these important concepts, which are used extensively in this course.
Browse courses on
Statistics
Show steps
-
Review the concepts of probability, distributions, and statistical inference.
-
Review the concepts of vectors, matrices, and linear transformations.
Organize and review course materials
Show steps
Keeping your course materials organized and reviewing them regularly will help you stay on track and improve your understanding of the material.
Show steps
-
Create a system for organizing your notes, assignments, and quizzes.
-
Review your notes and assignments regularly.
Review fundamentals of data engineering
Show steps
Refreshing your knowledge of the basics of data engineering will improve your comprehension of the concepts introduced in this course.
Browse courses on
Data Engineering
Show steps
-
Review the concepts of data collection, storage, and analysis.
-
Practice using basic data engineering tools and technologies.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Participate in a peer study group
Show steps
Participating in a peer study group will allow you to discuss course material with other students and clarify any misunderstandings.
Show steps
-
Find a group of students who are also taking this course.
-
Meet regularly to discuss the course material and work on assignments together.
Practice data extraction and feature selection techniques
Show steps
Regular practice of data extraction and feature selection techniques will help you develop proficiency in these skills, which are essential for success in this course.
Show steps
-
Find and download a publicly available dataset.
-
Use Python or R to extract data from the dataset.
-
Apply feature selection techniques to the extracted data.
-
Evaluate the results of your feature selection.
Start a project using AWS SageMaker
Show steps
Starting a project using AWS SageMaker will give you hands-on experience with the tools and technologies used in this course.
Show steps
-
Create an AWS account.
-
Set up AWS SageMaker notebook instance.
-
Explore the SageMaker user interface and documentation.
-
Create a simple machine learning model using SageMaker.
Use PCA for Feature Extraction
Show steps
Practice using Principal Component Analysis (PCA) to reduce dimensionality and extract features from real-world datasets, reinforcing your understanding of the technique.
Browse courses on
Feature Extraction
Show steps
-
Collect and preprocess a dataset with high dimensionality.
-
Apply PCA to the dataset using appropriate hyperparameters.
-
Analyze the transformed data to identify the most significant features.
Follow tutorials on advanced data engineering techniques
Show steps
Following tutorials will help you learn advanced data engineering techniques that are not covered in this course.
Show steps
-
Find tutorials on topics such as data integration, data mining, and machine learning.
-
Follow the tutorials and complete the exercises.
-
Apply the techniques you learned to your own projects.
Develop a data engineering solution for a real-world problem
Show steps
Developing a data engineering solution for a real-world problem will give you hands-on experience and allow you to apply the skills you learned in this course.
Show steps
-
Identify a real-world problem that can be solved using data engineering.
-
Design a data engineering solution that meets the requirements of the problem.
-
Implement the solution using appropriate tools and technologies.
-
Evaluate the performance of the solution and make improvements as necessary.
Review basic statistics and linear algebra
Show steps
Reviewing basic statistics and linear algebra will help you refresh your knowledge of these important concepts, which are used extensively in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review the concepts of probability, distributions, and statistical inference.
- Review the concepts of vectors, matrices, and linear transformations.
Organize and review course materials
Show steps
Keeping your course materials organized and reviewing them regularly will help you stay on track and improve your understanding of the material.
Show steps
Show steps
Show steps
- Create a system for organizing your notes, assignments, and quizzes.
- Review your notes and assignments regularly.
Review fundamentals of data engineering
Show steps
Refreshing your knowledge of the basics of data engineering will improve your comprehension of the concepts introduced in this course.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Review the concepts of data collection, storage, and analysis.
- Practice using basic data engineering tools and technologies.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Participate in a peer study group
Show steps
Participating in a peer study group will allow you to discuss course material with other students and clarify any misunderstandings.
Show steps
Show steps
Show steps
- Find a group of students who are also taking this course.
- Meet regularly to discuss the course material and work on assignments together.
Practice data extraction and feature selection techniques
Show steps
Regular practice of data extraction and feature selection techniques will help you develop proficiency in these skills, which are essential for success in this course.
Show steps
Show steps
Show steps
- Find and download a publicly available dataset.
- Use Python or R to extract data from the dataset.
- Apply feature selection techniques to the extracted data.
- Evaluate the results of your feature selection.
Start a project using AWS SageMaker
Show steps
Starting a project using AWS SageMaker will give you hands-on experience with the tools and technologies used in this course.
Show steps
Show steps
Show steps
- Create an AWS account.
- Set up AWS SageMaker notebook instance.
- Explore the SageMaker user interface and documentation.
- Create a simple machine learning model using SageMaker.
Use PCA for Feature Extraction
Show steps
Practice using Principal Component Analysis (PCA) to reduce dimensionality and extract features from real-world datasets, reinforcing your understanding of the technique.
Browse courses on
Feature Extraction
Show steps
Show steps
Show steps
- Collect and preprocess a dataset with high dimensionality.
- Apply PCA to the dataset using appropriate hyperparameters.
- Analyze the transformed data to identify the most significant features.
Follow tutorials on advanced data engineering techniques
Show steps
Following tutorials will help you learn advanced data engineering techniques that are not covered in this course.
Show steps
Show steps
Show steps
- Find tutorials on topics such as data integration, data mining, and machine learning.
- Follow the tutorials and complete the exercises.
- Apply the techniques you learned to your own projects.
Develop a data engineering solution for a real-world problem
Show steps
Developing a data engineering solution for a real-world problem will give you hands-on experience and allow you to apply the skills you learned in this course.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using data engineering.
- Design a data engineering solution that meets the requirements of the problem.
- Implement the solution using appropriate tools and technologies.
- Evaluate the performance of the solution and make improvements as necessary.
Career center
Learners who complete Data Engineering in AWS will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses machine learning algorithms to solve real-world problems. They need to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and maintains machine learning models. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Machine Learning Engineer.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data pipelines. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Data Engineer.
Data Analyst
Data Analyst
A Data Analyst uses data to make informed decisions. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Data Analyst.
Business Analyst
Business Analyst
A Business Analyst uses data to identify and solve business problems. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Business Analyst.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Financial Analyst.
Product Manager
Product Manager
A Product Manager develops and manages products. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Product Manager.
Operations Manager
Operations Manager
An Operations Manager plans and executes operations. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become an Operations Manager.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze data. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Quantitative Analyst.
Marketing Manager
Marketing Manager
A Marketing Manager develops and executes marketing campaigns. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Marketing Manager.
Actuary
Actuary
An Actuary uses mathematical and statistical models to assess risk. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become an Actuary.
Project Manager
Project Manager
A Project Manager plans and executes projects. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Project Manager.
Sales Manager
Sales Manager
A Sales Manager leads and manages sales teams. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Sales Manager.
Statistician
Statistician
A Statistician designs and conducts statistical studies, analyzes data, and interprets the results. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Statistician.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They need to understand how to gather data, handle missing data, and perform feature extraction and selection. This course will help build a foundation in these concepts, making it a great starting point for anyone who wants to become a Software Engineer.
For more career information including salaries, visit:
OpenCourser.com/course/8niwqu/data
Reading list
We've selected 17 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Engineering in AWS.
Provides a comprehensive overview of Machine Learning with AWS, including a guide to using SageMaker.
A comprehensive guide to deep learning using Python, providing theoretical and practical knowledge.
A hands-on guide to machine learning using popular Python libraries, offering practical insights.
Provides techniques for interpreting machine learning models, helping to understand and validate their predictions.
An introduction to machine learning using Python, providing a practical approach.
Provides a comprehensive guide to data engineering with Python. It covers all aspects of data engineering, from data ingestion to data transformation to data analysis. It hands-on guide that includes many code examples.
Provides a comprehensive guide to feature engineering for machine learning. It covers all aspects of feature engineering, from data exploration to feature selection to feature transformation. It hands-on guide that includes many code examples.
Provides a comprehensive guide to machine learning with R. It covers all aspects of machine learning, from data exploration to model building to model evaluation. It hands-on guide that includes many code examples.
Provides a comprehensive guide to natural language processing with Python. It covers all aspects of natural language processing, from text preprocessing to text classification to text generation. It hands-on guide that includes many code examples.
Provides a comprehensive guide to computer vision with Python. It covers all aspects of computer vision, from image processing to object detection to image classification. It hands-on guide that includes many code examples.
Provides a comprehensive guide to data science for business. It covers all aspects of data science, from data collection to data analysis to data visualization. It hands-on guide that includes many code examples.
Save
Provides a comprehensive guide to deep learning. It covers all aspects of deep learning, from neural networks to convolutional neural networks to recurrent neural networks. It theoretical guide that includes many mathematical equations.
Provides a comprehensive guide to reinforcement learning. It covers all aspects of reinforcement learning, from Markov decision processes to Q-learning to policy gradients. It theoretical guide that includes many mathematical equations.
Save
Provides a comprehensive guide to data mining. It covers all aspects of data mining, from data preprocessing to data clustering to data classification. It theoretical guide that includes many mathematical equations.
Save
Provides a comprehensive guide to big data. It covers all aspects of big data, from data collection to data analysis to data visualization. It non-technical guide that is accessible to readers of all levels.
Provides a comprehensive guide to data science. It covers all aspects of data science, from data collection to data analysis to data visualization. It hands-on guide that includes many code examples.
Provides a comprehensive guide to data analytics. It covers all aspects of data analytics, from data collection to data analysis to data visualization. It non-technical guide that is accessible to readers of all levels.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8niwqu/data
Share
Similar courses
Here are nine courses similar to
Data Engineering in AWS.
Preparing Data for Feature Engineering and Machine Learning
OpenCourser.com/course/rbi06y/preparing
Preparing Data for Feature Engineering and Machine...
Most relevant
Building Features from Image Data
OpenCourser.com/course/98qu2n/building
Building Features from Image Data
Most relevant
ML Algorithms
OpenCourser.com/course/2xcg6o/ml
ML Algorithms
Most relevant
Feature Selection and Extraction in Microsoft Azure
OpenCourser.com/course/yv72fn/feature
Feature Selection and Extraction in Microsoft Azure
Most relevant
AWS: Security in Data Analytics
OpenCourser.com/course/le2c0k/aws
AWS: Security in Data Analytics
Computer Vision Fundamentals with Google Cloud
OpenCourser.com/course/inyedm/computer
Computer Vision Fundamentals with Google Cloud
Machine Learning Implementation and Operations in AWS
OpenCourser.com/course/9szyba/machine
Machine Learning Implementation and Operations in AWS
AWS: Data Management and Backups
OpenCourser.com/course/490a2g/aws
AWS: Data Management and Backups
MLOps Platforms: Amazon SageMaker and Azure ML
OpenCourser.com/course/jq0kjn/mlops
MLOps Platforms: Amazon SageMaker and Azure ML
Effort
2 Weeks of Study, 2-2:30 hours/Week
Level
Introductory
Via
Coursera
Institution
Whizlabs
Instructor
Whizlabs Instructor
Language
English
This course belongs to a
collection
called
Exam Prep MLS-C01: AWS Certified Specialty Machine Learning. It's comprised of five courses:
- ML Algorithms
- Machine Learning Implementation and Operations in AWS
- Exploratory Data Analysis in AWS
- Data Engineering in AWS (viewing)
- Modeling in AWS
Good to know
Provides a foundational understanding of data engineering techniques in AWS, which is standard in industry
Taught by Whizlabs Instructor, who are recognized for their work in AWS data engineering
Develops skills in data gathering, missing data handling, feature extraction, and feature selection, which are core skills for data engineering in AWS
Examines techniques for handling missing data, which is a common challenge in data engineering
Explores feature extraction and feature selection techniques, which are essential for data transformation
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser