Getting Started With AWS For Data Science

  • BeginnerLevel

  • 100+Students Enrolled

  • 2 Hrs 15 MinsDuration

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About this Course

  • Learn AWS from a data science lens: cloud basics, why scaling matters for ML workloads, and how AWS fits into real DS workflows from storage to compute.
  • Get practical with core services: understand S3 for datasets, EC2 for compute (including GPU instances), and the role of managed services like SageMaker.
  • Set up securely from day one: create an AWS account, protect the root user, create IAM users, and learn beginner-friendly permission concepts for safe access.
  • Work like a practitioner: choose between Console, CLI, and SDK, set up AWS CLI, connect S3 with notebooks, and learn cost basics to avoid surprises.

Learning Outcomes

AWS Foundations for DS

Explain cloud basics and map AWS services to DS workflows.

Core Services Mastery

Use S3, EC2, IAM and understand SageMaker's role in ML.

Operate AWS Like a Pro

Work via Console, CLI, SDK with cost-aware best practices.

Who Should Enroll

  • Beginners in data science or analytics who want to learn AWS basics and move beyond local-only workflows.
  • Students and freshers preparing for DS/ML roles who need practical exposure to S3, EC2, IAM, and CLI.
  • Working professionals starting cloud adoption who want a clear, guided path to use AWS safely and cost-aware.

Course Curriculum

Learn AWS fundamentals for data science: cloud basics, core services (S3, EC2/GPU, IAM, Sagemaker), security setup, Console/CLI/SDK usage, cost basics, plus hands-on S3 with notebooks.

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  1. 1. What is Cloud Computing & AWS?

  2. 2. Why AWS matters in Data Science?

  1. 1. AWS Core Ecosystem Overview

  2. 2. Amazon S3 for Data Science

  3. 3. Amazon EC2 for Compute & GPUs

  4. 4. IAM Fundamentals

  5. 5. Introduction to Sagemaker

  1. 1. Exploring the AWS Console

  2. 2. Creating an AWS Account

  3. 3. Different Interfaces to Interact with AWS Services

  4. 4. Creating IAM Users

  1. 1. Introduction to AWS Console

  2. 2. Introduction to AWS CLI

  3. 3. Managing AWS Costs

  1. 1. SDK & Notebook Integration

  2. 2. Hands On: AWS CLI Setup & Configuration

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
Jatin Goel

DevOps Mentor

Jain Goel is a Cloud Architect, DevOps Engineer, and Data Analyst with 10+ years of experience. A freelance DevOps mentor, he shares practical learnings and is a results-driven Project Lead and technical trainer with strong leadership.

Get this Course Now

With this course you’ll get

  • 2 Hours 15 Mins

    Duration

  • Jatin Goel

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

You will learn the essentials of Amazon S3 for storage, Amazon EC2 for compute (including GPU instances), IAM for security and access control, and a beginner-friendly introduction to Amazon SageMaker for ML workflows.

AWS helps you store large datasets reliably, scale compute for training models, and run workloads beyond your laptop limits. It also introduces production-ready workflows that companies commonly use for data and ML projects.

Cloud computing means you rent storage and compute on demand instead of relying on your local machine. For data science, it enables larger datasets, faster training, team collaboration, and repeatable workflows that scale when your experiments grow.

Scaling means increasing resources like CPU, RAM, storage, or GPUs when workload demand rises. ML training and data processing can quickly outgrow laptops, so scaling helps you run bigger jobs faster without rewriting everything from scratch.

S3 is durable, cheap, and designed for storing massive amounts of data. It becomes the central place for raw datasets, processed files, and model artifacts, making it easy for notebooks, EC2, and ML services to access consistent data.

EC2 gives you full control, but you manage setup, environments, and scaling. SageMaker provides built-in tools for training, deployment, and monitoring. It streamlines ML operations when you want structured, production-friendly workflows.

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