AWS Data Science Complete track

Table of Contents

Part I: Introduction to AWS and Data Science
1. Understanding AWS: Basics and Overview
– Introduction to Amazon Web Services (AWS)
– AWS Global Infrastructure
– AWS Core Services
2. Introduction to Data Science
– What is Data Science?
– Role of Data Science in Business
– Data Science Lifecycle

Part II: AWS Fundamentals for Data Science
3. AWS Services for Data Science
– Amazon S3 (Simple Storage Service)
– Amazon EC2 (Elastic Compute Cloud)
– Amazon RDS (Relational Database Service)
– Amazon Redshift
– Amazon Athena
– AWS Glue
4. Setting Up AWS Environment for Data Science
– Creating an AWS Account
– Setting Up AWS CLI (Command Line Interface)
– IAM (Identity and Access Management) Basics
– Configuring Security Groups and Key Pairs

Part III: Data Acquisition and Preparation on AWS
5. Data Collection and Ingestion
– Streaming Data with Amazon Kinesis
– Batch Data Ingestion with AWS Data Pipeline
– Data Import/Export with AWS Snowball
6. Data Storage and Management
– Data Storage Options on AWS
– Working with Amazon S3 for Data Storage
– Setting up and Managing Amazon RDS
– Data Warehousing with Amazon Redshift

Part IV: Data Processing and Analysis on AWS
7. Data Preprocessing and Cleaning
– Data Transformation with AWS Glue
– Data Cleansing Techniques
8. Exploratory Data Analysis (EDA)
– Visualizing Data with Amazon QuickSight
– Statistical Analysis with AWS Services
– Exploratory Data Analysis Techniques
9. Machine Learning on AWS
– Introduction to Amazon SageMaker
– Building ML Models with SageMaker
– Model Training and Evaluation
– Model Deployment with SageMaker Endpoints

Part V: Advanced Topics in AWS Data Science
10. Big Data Analytics with AWS
– Introduction to Big Data on AWS
– Working with Amazon EMR (Elastic MapReduce)
– Real-time Analytics with AWS Lambda and Kinesis
11. Advanced Data Science Techniques
– Deep Learning on AWS with Amazon SageMaker
– Natural Language Processing (NLP) on AWS
– Time Series Analysis on AWS
12. Optimizing Data Science Workflows on AWS
– Cost Optimization Strategies
– Performance Optimization Techniques
– Scalability and Elasticity in Data Science Projects

Part VI: Case Studies and Projects
13. Case Studies in AWS Data Science
– Real-world Examples of Data Science Projects on AWS
– Industry-specific Case Studies (e.g., Healthcare, Finance, E-commerce)
14. Capstone Project
– Designing and Implementing a Data Science Project on AWS
– Project Presentation and Documentation

Part VII: Future Trends and Best Practices
15. Emerging Trends in AWS Data Science
– Edge Computing and IoT Integration
– AI/ML Automation
– Ethical Considerations in Data Science
16. Best Practices in AWS Data Science
– Security Best Practices
– Performance Optimization Tips
– Collaboration and Teaamwork in Data Science Projects

Appendix
– Glossary of AWS and Data Science Terms
– Additional Resources and Further Reading

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This comprehensive track covers everything from foundational AWS concepts to advanced data science techniques, providing learners with a thorough understanding of how to leverage AWS for data-driven insights and solutions.