AWS for Data Science: EC2 vs. SageMaker vs. Lambda
BeginnerLevel
121+Students Enrolled
1 HrDuration
4.8Average Rating

About this Course
- Learn the core differences between EC2, SageMaker, and Lambda from a data science practitioner’s perspective.
- Follow a practical EC2 setup flow and see how instance configuration impacts your data science workflow.
- Walk through a hands-on implementation using an EC2 launch script and Jupyter notebook.
- Get a beginner-friendly introduction to SageMaker notebooks and where they fit in managed ML workflows.
Course Benefits
- Choose the right AWS compute service for data science tasks with more confidence and less trial-and-error.
- Understand the trade-offs between EC2, SageMaker, and Lambda for cost, control, and scalability.
- Gain hands-on experience launching an EC2 setup and running a notebook workflow end-to-end.
- Build a practical foundation for deploying and testing data science workflows on AWS services.
Learning Outcomes
Compute Overview
Understand EC2, SageMaker, and Lambda for data science tasks.
Service Selection
Learn to choose compute options based on workload, cost, and control.
EC2 Setup & Configuration
Launch & configure an EC2 instance for notebook-based experimentation.
End-to-End Execution
Run a complete notebook workflow using an EC2-based setup.
Who Should Enroll
- Data science beginners who want to understand AWS compute choices before deploying ML workflows.
- Analysts and aspiring ML practitioners moving from local notebooks to cloud-based experimentation.
- Learners who want a practical EC2 setup walkthrough with script-based launching and notebook execution.
- Professionals comparing managed vs self-managed AWS options for data science and prototyping tasks.
Course Curriculum
Learn how AWS compute options fit data science workflows by comparing EC2, SageMaker, and Lambda. Then build a practical EC2 setup, review SageMaker notebooks, and run an end-to-end notebook workflow hands-on.
Build a strong foundation for AWS compute in data science by comparing Lambda, EC2, and SageMaker across real use cases, control, scaling, and cost. You will also understand the EC2 setup journey and what is required before running notebook-based workflows.
1. Compute Options for Data Science: Lambda vs EC2 vs SageMaker
2. EC2 Setup Flow: From Concept to Instance Preparation
3. EC2 Setup Flow: From Concept to Instance Preparation
Move from concepts to execution by launching an EC2 instance through a script and understanding key inputs like AMI selection and security groups. Then explore SageMaker notebooks and run a guided Jupyter workflow end-to-end in a practical setup.
1. Launching EC2 with a Script: Parameters, AMI, and Security Groups
2. SageMaker Notebooks Overview
3. Hands-On in Jupyter: Run the Workflow End-to-End
Meet the instructor
Our instructor and mentors carry years of experience in data industry
Get this Course Now
With this course you’ll get
- 1 Hour
Duration
- Jatin Goel
Instructor
- Beginner
Level
Certificate of completion
Earn a professional certificate upon course completion
- Industry-Recognized Credential
- Career Advancement Credential
- Shareable Achievement

Frequently Asked Questions
Looking for answers to other questions?
You will learn how EC2, SageMaker, and Lambda differ for data science workloads, when to use each one, and how to run a practical EC2-based notebook workflow.
Yes. It is designed for beginners who are new to AWS compute services, with explanations focused on practical usage for data science.
It is practical and workflow-driven. The course includes an EC2 launch script walkthrough, notebook execution steps, and a guided comparison of compute options.
Basic Python and notebook familiarity will help, but the walkthrough is beginner-friendly and explains the important setup steps clearly.
Yes. The course introduces real AWS services used in data science workflows, including EC2, SageMaker, and Lambda, with a practical EC2 implementation flow.
Yes. The compute selection framework and setup guidance are directly useful when choosing infrastructure for experimentation, prototyping, and lightweight ML workflows.
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