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Exploratory Data Analysis in AWS

Exploratory Data Analysis in AWS is the second course in the AWS Certified Machine Learning Specialty specialization. The main focus of this course is to analyze Data Streams and Data Analytics services in AWS along with exploring Data Analysis in AWS. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:00-2:30 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 Streams and Data Analytics services in AWS

Module 2: Exploring Data Analysis in AWS

By the end of this course, a learner will be able to:

-Demonstrate the implementation of Kinesis Data Streams

-Analyze and visualize data for machine learning

-Examine AWS Glue service with creation of crawler and transform job

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What's inside

Syllabus

Introduction to Data Streams and Data Analytics services in AWS

Module 1:Introduction to Data Streams and Data Analytics services in AWS Welcome to Week 1 of Exploratory Data Analysis in AWS Course. In this week, we’ll Analyze working of Kinesis Data Streams and Kinesis Data Firehose. We’ll also gain demonstrations on Kinesis Data Streams and Kinesis Data Firehose. Finally, the week will end by implementing Kinesis Video Streams and Kinesis Data Analytics.

Exploring Data Analysis in AWS

Welcome to Week 2 of Exploratory Data Analysis in AWS Course. This week, we’ll learn AWS Glue Service for performing Data Analysis. We’ll be able to Analyze and visualize data for machine learning. In the end of the week, we’ll Demonstrate AWS Glue Crawler and Create transform job.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills in working with data for machine learning through AWS services

Provides a hands-on learning experience through video lectures and graded quizzes

Covers foundational concepts of data streams and data analytics in AWS

Introduces learners to the AWS Glue service for data analysis

May require prior knowledge of data analysis and AWS services

The content might be outdated if there are significant updates to AWS services

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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 in AWS with these activities:

Review Data Analysis Fundamentals

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Revisit the fundamental concepts of data analysis, statistics, and Python programming to strengthen your foundation before embarking on this course.

Browse courses on Data Analysis

Show steps

Review basic data analysis concepts
Refresh your understanding of statistics
Practice using Python for data analysis

Course Materials Compilation

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Organize and review your class notes, assignments, and other course materials to enhance your understanding and retention of the course content.

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Gather and organize your course materials
Review and summarize key concepts
Create a study guide or cheat sheet for reference

Study Group Discussions

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Join a study group to engage with peers, share insights, and reinforce your understanding of the course concepts through collaborative learning.

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Find or form a study group
Meet regularly to discuss course topics
Collaborate on assignments and projects

Four other activities

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Show all seven activities

AWS Glue Tutorials

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Follow AWS Glue tutorials to gain practical experience with data transformations and integration, enhancing your understanding of data analysis techniques in AWS.

Browse courses on AWS Glue

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Complete the AWS Glue Getting Started tutorial
Explore additional AWS Glue tutorials based on your interests

Data Streams Lab Exercises

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Practice the implementation and configuration of data streams services in AWS to reinforce your understanding of data streaming concepts.

Browse courses on Kinesis Data Streams

Show steps

Create a Kinesis Data Stream
Write data to the Kinesis Data Stream
Create a Kinesis Data Analytics application
Analyze data in the Kinesis Data Analytics application

Data Analysis Dashboard

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Create a comprehensive data analysis dashboard to visualize and analyze data for your chosen use case, solidifying your understanding of data analysis techniques.

Browse courses on Data Analysis

Show steps

Gather and prepare your data
Create a data visualization framework
Design and develop your dashboard
Publish and share your dashboard

Machine Learning Project with AWS Data Analytics Services

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Embark on a project that leverages AWS data analytics services to build and train a machine learning model, applying your knowledge of data analysis and machine learning in a practical setting.

Browse courses on Machine Learning

Show steps

Define your project goals and scope
Gather and prepare your data
Build and train your machine learning model
Evaluate and refine your model
Deploy your model and monitor its performance

Career center

Learners who complete Exploratory Data Analysis in AWS will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst is responsible for collecting, cleaning, analyzing, and interpreting data to help businesses make informed decisions. This course can help you develop the skills needed to be a successful Data Analyst, such as data wrangling, data visualization, and statistical analysis. Additionally, the course covers AWS-specific tools and services that are commonly used by Data Analysts, such as Kinesis Data Streams and AWS Glue.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer is responsible for designing, building, and maintaining data pipelines and data infrastructure. This course can help you develop the skills needed to be a successful Data Engineer, such as data modeling, data integration, and cloud computing. Additionally, the course covers AWS-specific tools and services that are commonly used by Data Engineers, such as Kinesis Data Firehose and AWS Glue.

See salaries and explore the career path for Data Engineer

Data Scientist

A Data Scientist is responsible for using data to solve business problems. This course can help you develop the skills needed to be a successful Data Scientist, such as machine learning, data mining, and predictive analytics. Additionally, the course covers AWS-specific tools and services that are commonly used by Data Scientists, such as Kinesis Data Analytics and AWS SageMaker.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer is responsible for designing, building, and deploying machine learning models. This course can help you develop the skills needed to be a successful Machine Learning Engineer, such as machine learning algorithms, model evaluation, and cloud computing. Additionally, the course covers AWS-specific tools and services that are commonly used by Machine Learning Engineers, such as Kinesis Data Streams and AWS SageMaker.

See salaries and explore the career path for Machine Learning Engineer

Business Intelligence Analyst

A Business Intelligence Analyst is responsible for using data to make informed business decisions. This course can help you develop the skills needed to be a successful Business Intelligence Analyst, such as data visualization, data mining, and predictive analytics. Additionally, the course covers AWS-specific tools and services that are commonly used by Business Intelligence Analysts, such as Kinesis Data Streams and AWS QuickSight.

See salaries and explore the career path for Business Intelligence Analyst

Statistician

A Statistician is responsible for collecting, analyzing, and interpreting data. This course can help you develop the skills needed to be a successful Statistician, such as statistical analysis, data visualization, and probability theory. Additionally, the course covers AWS-specific tools and services that are commonly used by Statisticians, such as Kinesis Data Streams and Amazon Redshift.

See salaries and explore the career path for Statistician

Data Architect

A Data Architect is responsible for designing and managing data systems. This course can help you develop the skills needed to be a successful Data Architect, such as data modeling, data integration, and cloud computing. Additionally, the course covers AWS-specific tools and services that are commonly used by Data Architects, such as Kinesis Data Streams and AWS Glue.

See salaries and explore the career path for Data Architect

Database Administrator

A Database Administrator is responsible for managing and maintaining databases. This course can help you develop the skills needed to be a successful Database Administrator, such as database design, database optimization, and cloud computing. Additionally, the course covers AWS-specific tools and services that are commonly used by Database Administrators, such as Amazon RDS and Amazon DynamoDB.

See salaries and explore the career path for Database Administrator

Software Engineer

A Software Engineer is responsible for designing, building, and maintaining software applications. This course can help you develop the skills needed to be a successful Software Engineer, such as software design, software development, and cloud computing. Additionally, the course covers AWS-specific tools and services that are commonly used by Software Engineers, such as Kinesis Data Streams and AWS Lambda.

See salaries and explore the career path for Software Engineer

Cloud Architect

A Cloud Architect is responsible for designing and managing cloud computing systems. This course can help you develop the skills needed to be a successful Cloud Architect, such as cloud computing, cloud security, and cloud optimization. Additionally, the course covers AWS-specific tools and services that are commonly used by Cloud Architects, such as Kinesis Data Streams and AWS CloudFormation.

See salaries and explore the career path for Cloud Architect

DevOps Engineer

A DevOps Engineer is responsible for bridging the gap between development and operations teams. This course can help you develop the skills needed to be a successful DevOps Engineer, such as cloud computing, continuous integration, and continuous delivery. Additionally, the course covers AWS-specific tools and services that are commonly used by DevOps Engineers, such as Kinesis Data Streams and AWS CodePipeline.

See salaries and explore the career path for DevOps Engineer

Big Data Engineer

A Big Data Engineer is responsible for designing, building, and maintaining big data systems. This course can help you develop the skills needed to be a successful Big Data Engineer, such as big data technologies, big data analytics, and cloud computing. Additionally, the course covers AWS-specific tools and services that are commonly used by Big Data Engineers, such as Kinesis Data Streams and Amazon EMR.

See salaries and explore the career path for Big Data Engineer

Data Visualization Engineer

A Data Visualization Engineer is responsible for designing and creating data visualizations. This course can help you develop the skills needed to be a successful Data Visualization Engineer, such as data visualization techniques, data visualization tools, and cloud computing. Additionally, the course covers AWS-specific tools and services that are commonly used by Data Visualization Engineers, such as Kinesis Data Streams and Amazon QuickSight.

See salaries and explore the career path for Data Visualization Engineer

Machine Learning Scientist

A Machine Learning Scientist is responsible for conducting research and developing machine learning algorithms. This course may help you develop some of the skills needed to be a successful Machine Learning Scientist, such as machine learning algorithms and machine learning theory. However, this course does not cover advanced topics such as natural language processing or computer vision.

See salaries and explore the career path for Machine Learning Scientist

Data Security Analyst

A Data Security Analyst is responsible for protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction. This course may help you develop some of the skills needed to be a successful Data Security Analyst, such as data security principles and data security technologies. However, this course does not cover advanced topics such as intrusion detection or security auditing.

See salaries and explore the career path for Data Security Analyst

Reading list

We've selected six 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 in AWS.

Python for Data Analysis

Save

Will help learners to understand the basics of data analysis in Python using Pandas.

Python for Data Analysis: Data Wrangling with...

Paperback

Python for Data Analysis

Kindle Edition

Python for Data Analysis: Data Wrangling with...

Paperback

$$$

Machine Learning with PyTorch and Scikit-Learn

Save

Covers the basics of machine learning using Python and scikit-learn.

Machine Learning with PyTorch and Scikit-Learn:...

Kindle Edition

Natural Language Processing with Python

Save

Covers natural language processing in Python using NLTK.

Natural Language Processing with Python: Analyzing...

Paperback

$$$

(中文) Python Natural Language Processing(Chinese Edition)

Paperback

Natural Language Processing with Python: Analyzing...

Kindle Edition

Deep Learning and XAI Techniques for Anomaly...

Save

Provides a beginner-friendly introduction to AWS services, useful for learners who are new to AWS.

Deep Learning and XAI Techniques for Anomaly...

Kindle Edition

Advanced Analytics with PySpark

Save

Covers advanced data analytics techniques using Spark.

Advanced Analytics with PySpark: Patterns for...

Paperback

Advanced Analytics with PySpark

Kindle Edition

Machine Learning with Python Cookbook

Save

Provides a comprehensive overview of machine learning with AWS. It covers a wide range of topics, including data preparation, model training, and model deployment.

Machine Learning with Python Cookbook: Practical...

Paperback

Machine Learning with Python Cookbook

Kindle Edition

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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 (viewing)
Data Engineering in AWS
Modeling in AWS

Good to know

Develops skills in working with data for machine learning through AWS services

Provides a hands-on learning experience through video lectures and graded quizzes

Covers foundational concepts of data streams and data analytics in AWS

Introduces learners to the AWS Glue service for data analysis

May require prior knowledge of data analysis and AWS services

The content might be outdated if there are significant updates to AWS services

Share and help others discover this course.

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