We may earn an affiliate commission when you visit our partners.
Mohammed Osman
Understanding how to deal with data is becoming a required skill in the information age. This course will teach you how to do exploratory data analysis and leverage relevant AWS services.
Read more
Understanding how to deal with data is becoming a required skill in the information age. This course will teach you how to do exploratory data analysis and leverage relevant AWS services.
Read more
Understanding how to deal with data is becoming a required skill in the information age. This course will teach you how to do exploratory data analysis and leverage relevant AWS services.
Understanding underlying trends and outliers in data is a necessary step to do proper data preparation and feature engineering for subsequent machine learning tasks.
In this course, Exploratory Data Analysis with AWS Machine Learning, you’ll learn how to analyze, visualize, preprocess and feature engineer datasets to make them ready for subsequent machine learning steps.
What you will learn in this
:
Register for this course and see more details by visiting:
OpenCourser.com/course/ul3uor/exploratory
Here's a deal for you
We found an offer that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
Course Overview
Machine Learning with AWS
Data Analysis Using AWS
Data Visualization Using AWS
Read more
Syllabus
Course Overview
Machine Learning with AWS
Data Analysis Using AWS
Data Visualization Using AWS
Read more
Data Preparation Using AWS
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches essential data preparation and feature engineering steps to prepare data for machine learning models
Instructed by Mohammed Osman, an expert in data analysis and AWS machine learning
Leverages industry-standard AWS services for data visualization and analysis
Emphasizes understanding underlying data trends and outliers for effective data preparation
Introduces exploratory data analysis techniques and their applications in various fields
Prerequisites may be required to fully grasp the advanced concepts covered in the course
Save this course
Save Exploratory Data Analysis with AWS Machine Learning 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 Exploratory Data Analysis with AWS Machine Learning with these
activities:
Organize Course Materials
Show steps
Organize your notes, assignments, quizzes, and exams to improve your understanding and retention of the course material.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
File and organize materials according to topic or module.
Review Prior Mathematics
Show steps
Review the key algebra and statistics topics needed to be successful in this course.
Browse courses on
Algebra
Show steps
-
Review linear equations and matrix manipulations.
-
Review basic statistical procedures for data visualization.
-
Review the basics of calculus, including derivatives and integrals.
Form a Study Group
Show steps
Join or form a study group to discuss course concepts and work together on assignments and projects.
Browse courses on
Exploratory Data Analysis
Show steps
-
Identify classmates who are interested in forming a study group.
-
Set regular meeting times and establish a communication plan.
-
Collaborate on assignments, review each other's work, and engage in discussions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend AWS Workshops
Show steps
Participate in AWS workshops to gain hands-on experience with AWS services for data analysis and visualization.
Browse courses on
Data Analysis
Show steps
-
Identify AWS workshops relevant to the course topics.
-
Register for and attend the workshops.
-
Actively participate in the exercises and discussions.
Follow Tutorials on AWS
Show steps
Supplement your learning by following guided tutorials on AWS services to gain hands-on experience.
Browse courses on
AWS
Show steps
-
Find tutorials on AWS services relevant to the course topics.
-
Follow the tutorials step-by-step and apply the concepts to practice datasets.
Solve Data Analysis Problems
Show steps
Solve data analysis problems on platforms like LeetCode or HackerRank to test your understanding and develop problem-solving skills.
Browse courses on
Exploratory Data Analysis
Show steps
-
Identify online platforms that offer data analysis problems.
-
Select problems that align with the concepts covered in the course.
-
Solve the problems using the techniques learned in the course.
-
Analyze your solutions and identify areas for improvement.
Develop a Data Analysis Plan
Show steps
Create a comprehensive plan outlining your approach to data analysis for a given dataset, including data exploration, visualization, and feature engineering.
Show steps
-
Define the goals and objectives of the data analysis.
-
Identify the relevant data sources and gather the necessary data.
-
Develop a strategy for data exploration and visualization.
-
Plan for feature engineering and data transformation.
-
Write a detailed report outlining your plan.
Build a Data Analysis Portfolio
Show steps
Develop a portfolio of data analysis projects to showcase your skills and understanding of the concepts covered in the course.
Show steps
-
Identify datasets that are relevant to your interests or career goals.
-
Apply the data analysis techniques learned in the course to these datasets.
-
Create visualizations and reports to present your findings.
-
Document your projects and share them on platforms like GitHub.
Organize Course Materials
Show steps
Organize your notes, assignments, quizzes, and exams to improve your understanding and retention of the course material.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- File and organize materials according to topic or module.
Review Prior Mathematics
Show steps
Review the key algebra and statistics topics needed to be successful in this course.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Review linear equations and matrix manipulations.
- Review basic statistical procedures for data visualization.
- Review the basics of calculus, including derivatives and integrals.
Form a Study Group
Show steps
Join or form a study group to discuss course concepts and work together on assignments and projects.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Identify classmates who are interested in forming a study group.
- Set regular meeting times and establish a communication plan.
- Collaborate on assignments, review each other's work, and engage in discussions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend AWS Workshops
Show steps
Participate in AWS workshops to gain hands-on experience with AWS services for data analysis and visualization.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify AWS workshops relevant to the course topics.
- Register for and attend the workshops.
- Actively participate in the exercises and discussions.
Follow Tutorials on AWS
Show steps
Supplement your learning by following guided tutorials on AWS services to gain hands-on experience.
Browse courses on
AWS
Show steps
Show steps
Show steps
- Find tutorials on AWS services relevant to the course topics.
- Follow the tutorials step-by-step and apply the concepts to practice datasets.
Solve Data Analysis Problems
Show steps
Solve data analysis problems on platforms like LeetCode or HackerRank to test your understanding and develop problem-solving skills.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Identify online platforms that offer data analysis problems.
- Select problems that align with the concepts covered in the course.
- Solve the problems using the techniques learned in the course.
- Analyze your solutions and identify areas for improvement.
Develop a Data Analysis Plan
Show steps
Create a comprehensive plan outlining your approach to data analysis for a given dataset, including data exploration, visualization, and feature engineering.
Show steps
Show steps
Show steps
- Define the goals and objectives of the data analysis.
- Identify the relevant data sources and gather the necessary data.
- Develop a strategy for data exploration and visualization.
- Plan for feature engineering and data transformation.
- Write a detailed report outlining your plan.
Build a Data Analysis Portfolio
Show steps
Develop a portfolio of data analysis projects to showcase your skills and understanding of the concepts covered in the course.
Show steps
Show steps
Show steps
- Identify datasets that are relevant to your interests or career goals.
- Apply the data analysis techniques learned in the course to these datasets.
- Create visualizations and reports to present your findings.
- Document your projects and share them on platforms like GitHub.
Career center
Learners who complete Exploratory Data Analysis with AWS Machine Learning will develop knowledge and skills
that may be useful to these careers:
Database Administrator
Database Administrator
A Database Administrator will manage and maintain databases. This course can help you succeed as a Database Administrator by teaching you how to use AWS services to analyze and prepare data. This knowledge will help you build better databases and data management solutions.
Data Scientist
Data Scientist
A Data Scientist will use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
Data Engineer
Data Engineer
A Data Engineer will design, build, and maintain the infrastructure and systems that store and process data. This course can help you succeed as a Data Engineer by teaching you how to use AWS services to analyze and prepare data. This knowledge will help you build better data pipelines.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst will collect, analyze, and interpret data to help businesses make better decisions. This course can help you succeed as a Business Intelligence Analyst by teaching you how to use AWS services to analyze and visualize data. This knowledge will help you gain insights into data and make better recommendations.
Data Visualization Engineer
Data Visualization Engineer
A Data Visualization Engineer will design and develop data visualizations to help businesses communicate data insights. This course can help you succeed as a Data Visualization Engineer by teaching you how to use AWS services to visualize data. This knowledge will help you create better data visualizations and communicate data insights more effectively.
Statistician
Statistician
A Statistician will collect, analyze, interpret, and present data. This course can help you succeed as a Statistician by teaching you how to use AWS services to analyze data. This knowledge will help you gain insights into data and make better recommendations.
Data Architect
Data Architect
A Data Architect will design and implement data management solutions. This course can help you succeed as a Data Architect by teaching you how to use AWS services to analyze and prepare data. This knowledge will help you build better data pipelines and data management solutions.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer will design, develop, and deploy machine learning models to solve business problems. This course can help you succeed as a Machine Learning Engineer by teaching you how to use AWS services to analyze and visualize data. This knowledge will help you build better machine learning models.
Data Analyst
Data Analyst
A Data Analyst will explore, prepare, and visualize data to find opportunities for a business. This course can help you succeed as a Data Analyst because it teaches you how to use AWS services to do exploratory data analysis. This includes understanding underlying trends and outliers in data, which is a necessary step for proper data preparation.
Software Engineer
Software Engineer
A Software Engineer will design, develop, and maintain software applications. This course may be useful for Software Engineers who want to learn how to use AWS services to analyze and prepare data. This knowledge can help you build better software applications.
Project Manager
Project Manager
A Project Manager will plan, execute, and close projects. This course may be useful for Project Managers who want to learn how to use AWS services to analyze and prepare data. This knowledge can help you build better project plans and project management systems.
Business Analyst
Business Analyst
A Business Analyst will analyze business processes and systems to identify opportunities for improvement. This course may be useful for Business Analysts who want to learn how to use AWS services to analyze and prepare data. This knowledge can help you build better business processes and systems.
Marketing Manager
Marketing Manager
A Marketing Manager will develop and execute marketing campaigns. This course may be useful for Marketing Managers who want to learn how to use AWS services to analyze and prepare data. This knowledge can help you build better marketing campaigns and marketing management systems.
Product Manager
Product Manager
A Product Manager will manage the development and launch of new products. This course may be useful for Product Managers who want to learn how to use AWS services to analyze and prepare data. This knowledge can help you build better products and product management systems.
For more career information including salaries, visit:
OpenCourser.com/course/ul3uor/exploratory
Reading list
We've selected 11 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
Exploratory Data Analysis with AWS Machine Learning.
Provides a comprehensive introduction to data analysis using Python and open source tools. It covers data exploration, cleaning, and visualization, as well as more advanced topics such as machine learning and statistical modeling.
Provides a practical introduction to exploratory data analysis using R, a popular programming language for data science. It covers data exploration, visualization, and statistical modeling.
Save
Provides a comprehensive introduction to machine learning for beginners. It covers the basics of machine learning, including supervised and unsupervised learning, as well as more advanced topics such as deep learning.
Provides a comprehensive introduction to deep learning using Python, a popular programming language for machine learning. It covers the basics of deep learning, including convolutional neural networks and recurrent neural networks.
Provides a comprehensive introduction to data visualization using Python and JavaScript. It covers a wide range of topics, including data exploration, visualization, and interactive dashboards.
Provides a comprehensive introduction to machine learning using R, a popular programming language for data science. It covers a wide range of topics, including data exploration, feature engineering, model training, and deployment.
Provides a comprehensive introduction to machine learning using Python and popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data exploration, feature engineering, model training, and deployment.
Provides a practical introduction to machine learning for beginners. It covers the basics of machine learning, including supervised and unsupervised learning, as well as more advanced topics such as natural language processing and computer vision.
Provides a comprehensive introduction to artificial intelligence for beginners. It covers a wide range of topics, including machine learning, deep learning, and natural language processing.
Provides a comprehensive introduction to data science using Python. It covers a wide range of topics, including data exploration, visualization, machine learning, and deep learning.
Save
Provides a comprehensive introduction to data science using R, a popular programming language for data science. It covers a wide range of topics, including data exploration, visualization, machine learning, and deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ul3uor/exploratory
Share
Similar courses
Here are nine courses similar to
Exploratory Data Analysis with AWS Machine Learning.
Introduction to Machine Learning
OpenCourser.com/course/lx0699/introduction
Introduction to Machine Learning
Most relevant
Exploratory Data Analysis in AWS
OpenCourser.com/course/fwj1fy/exploratory
Exploratory Data Analysis in AWS
Most relevant
MLOps Platforms: Amazon SageMaker and Azure ML
OpenCourser.com/course/jq0kjn/mlops
MLOps Platforms: Amazon SageMaker and Azure ML
Most relevant
Get Familiar with ML basics in a Kaggle Competition
OpenCourser.com/course/ovfzfe/get
Get Familiar with ML basics in a Kaggle Competition
Most relevant
Geospatial Data Science: Statistics and Machine Learning I
OpenCourser.com/course/lkzek0/geospatial
Geospatial Data Science: Statistics and Machine Learning I
Most relevant
AWS Certified Machine Learning Specialty 2024 - Hands On!
OpenCourser.com/course/2m2x6t/aws
AWS Certified Machine Learning Specialty 2024 - Hands On!
Most relevant
Machine Learning with PySpark: Customer Churn Analysis
OpenCourser.com/course/97rkse/machine
Machine Learning with PySpark: Customer Churn Analysis
Most relevant
Principal Component Analysis with NumPy
OpenCourser.com/course/0u08id/principal
Principal Component Analysis with NumPy
Most relevant
Regression Analysis with Yellowbrick
OpenCourser.com/course/i4z3qz/regression
Regression Analysis with Yellowbrick
Most relevant
Time
136 hours
Level
Introductory
Via
Pluralsight
Instructor
Mohammed Osman
Language
English
This course belongs to a
collection
called
AWS Certified Machine Learning – Specialty (MLS-C01). It's comprised of six courses:
- Fundamentals of Machine Learning on AWS
- Exploratory Data Analysis with AWS Machine Learning (viewing)
- Modeling with AWS Machine Learning
- Implementing and Operating AWS Machine Learning Solutions
- Data Engineering with AWS Machine Learning
- Demystifying the AWS Certified Machine Learning Specialty Exam
Good to know
Teaches essential data preparation and feature engineering steps to prepare data for machine learning models
Instructed by Mohammed Osman, an expert in data analysis and AWS machine learning
Leverages industry-standard AWS services for data visualization and analysis
Emphasizes understanding underlying data trends and outliers for effective data preparation
Introduces exploratory data analysis techniques and their applications in various fields
Prerequisites may be required to fully grasp the advanced concepts covered in the course
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