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Aws for data science basics

Track :

Computer Science

Lessons no : 15

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What will you learn in this course?

Implement data collection and preprocessing techniques using AWS Glue and S3 for scalable data management
Build, train, and optimize machine learning models with AWS SageMaker for accurate data analysis
Deploy machine learning models on AWS SageMaker to enable real-time predictions and scalable applications
Utilize Amazon Redshift for efficient data warehousing and complex data analysis in cloud environments

Design and implement end-to-end data science pipelines leveraging AWS cloud services and tools
Analyze data insights and generate actionable reports using AWS analytics solutions
Scale data science workflows on AWS to handle large datasets and improve processing speed
Integrate AWS solutions to streamline data workflows, enhance decision-making, and support data-driven strategies

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Lessons | 15

Aws for Data science Basics Aws cloud computing for beginners aws tutorial

Aws for Data science Basics Aws cloud computing for beginners aws tutorial

Aws Storage for Data science Basics Aws cloud computing storage for beginners aws tutorial

Aws Storage for Data science Basics Aws cloud computing storage for beginners aws tutorial

AWS for data science EC2 Instance AWS EC2 instance Aws EC2 tutorial AWS EC2 instance creation

AWS for data science EC2 Instance AWS EC2 instance Aws EC2 tutorial AWS EC2 instance creation

Application deployment in AWS Step by Step AWS deployment tutorial AWS deployment strategies

Application deployment in AWS Step by Step AWS deployment tutorial AWS deployment strategies

AWS CLI tutorial AWS cli tutorial on windows AWS cli installastion on windows AWS cli login

AWS CLI tutorial AWS cli tutorial on windows AWS cli installastion on windows AWS cli login

AWS sagemaker introduction AWS sagemaker tutorial AWS sagemaker tutorial for beginners

AWS sagemaker introduction AWS sagemaker tutorial AWS sagemaker tutorial for beginners

Build train and deploy model in sagemaker sagemaker tutorial sagemaker pipeline

Build train and deploy model in sagemaker sagemaker tutorial sagemaker pipeline

AWS Sagemaker Canvas Demo Sagemaker Canvas tutorial Sagemaker Canvas workshop

AWS Sagemaker Canvas Demo Sagemaker Canvas tutorial Sagemaker Canvas workshop

How I cleared AWS CCP in 10 days AWS cloud practitioner exam questions

How I cleared AWS CCP in 10 days AWS cloud practitioner exam questions

3 ways to work with AWS aws console vs SDK vs CLI aws sdk python aws cli vs sdk

3 ways to work with AWS aws console vs SDK vs CLI aws sdk python aws cli vs sdk

EC2 security groups and boot scripts ec2 boot scripts ec2 instance connect ec2 ssh

EC2 security groups and boot scripts ec2 boot scripts ec2 instance connect ec2 ssh

AWS lambda for beginners AWS lambda tutorial AWS lambda python AWS lambda interview questions

AWS lambda for beginners AWS lambda tutorial AWS lambda python AWS lambda interview questions

Think data like data scientist Data Scientist mindset about data Data Scientist job

Think data like data scientist Data Scientist mindset about data Data Scientist job

Data Preparation tools in AWS AWS Athena and AWS Glue Preparing ML data in AWS

Data Preparation tools in AWS AWS Athena and AWS Glue Preparing ML data in AWS

How to Pass AWS ML specialty exam AWS machine learning specialty exam AWS certification

How to Pass AWS ML specialty exam AWS machine learning specialty exam AWS certification

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Dipanshu kumar

Computer science 2025-01-28

Shindeojas

Good 2025-01-25

Robert Nkuna

Nice videos 2024-12-22

Ramanathan S

GOOD INFORMATION, THANKYOU 2024-11-23

DEBJYOTI DUTTA

Really a great online course and I got many new skills to learn. 2024-11-20

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Aws for data science course, in this course explores AWS solutions like SageMaker for machine learning, Redshift for data warehousing, and Glue for data integration. Learn how to preprocess data, build and deploy models, and analyze insights efficiently. Ideal for data professionals aiming to leverage AWS to streamline workflows, scale applications, and enhance decision-making through cloud technologies. By the end, you'll be equipped to implement data science pipelines, from data collection to model deployment, on the AWS platform, enabling innovation and growth in various industries.