Course Outline

Introduction to AWS Cloud9 for Data Science

  • Overview of AWS Cloud9 features for data science
  • Setting up a data science environment in AWS Cloud9
  • Configuring Cloud9 for Python, R, and Jupyter Notebook

Data Ingestion and Preparation

  • Importing and cleaning data from various sources
  • Using AWS S3 for data storage and access
  • Preprocessing data for analysis and modeling

Data Analysis in AWS Cloud9

  • Exploratory data analysis using Python and R
  • Working with Pandas, NumPy, and data visualization libraries
  • Statistical analysis and hypothesis testing in Cloud9

Machine Learning Model Development

  • Building machine learning models using Scikit-learn and TensorFlow
  • Training and evaluating models in AWS Cloud9
  • Using SageMaker with Cloud9 for large-scale model development

Database Integration and Management

  • Integrating AWS RDS and Redshift with AWS Cloud9
  • Querying large datasets using SQL and Python
  • Handling big data with AWS services

Model Deployment and Optimization

  • Deploying machine learning models using AWS Lambda
  • Using AWS CloudFormation to automate deployment
  • Optimizing data pipelines for performance and cost-efficiency

Collaborative Development and Security

  • Collaborating on data science projects in Cloud9
  • Using Git for version control and project management
  • Security best practices for data and models in AWS Cloud9

Summary and Next Steps

Requirements

  • Basic understanding of data science concepts
  • Familiarity with Python programming
  • Experience with cloud environments and AWS services

Audience

  • Data scientists
  • Data analysts
  • Machine learning engineers
 28 Hours

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