AWS Cloud9 for Data Science

• 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

AWS Cloud9 offers a robust environment for data science, enabling users to build, test, and deploy data models using cloud-based tools. This course guides participants through setting up and managing a data science environment in AWS Cloud9, with a focus on integrating with AWS services for data storage, processing, and machine learning.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and analysts who wish to use AWS Cloud9 for streamlined data science workflows.

By the end of this training, participants will be able to:

• Set up a data science environment in AWS Cloud9.
• Perform data analysis using Python, R, and Jupyter Notebook in Cloud9.
• Integrate AWS Cloud9 with AWS data services like S3, RDS, and Redshift.
• Utilize AWS Cloud9 for machine learning model development and deployment.
• Optimize cloud-based workflows for data analysis and processing.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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