Course Outline
Introduction to AWS Cloud9 for Government Data Science
- Overview of AWS Cloud9 features tailored for government data science initiatives
- Setting up a secure data science environment in AWS Cloud9 for government use
- Configuring Cloud9 to support Python, R, and Jupyter Notebook for government applications
Data Ingestion and Preparation for Government
- Importing and cleaning data from various sources in compliance with government standards
- Utilizing AWS S3 for secure data storage and access within government workflows
- Preprocessing data to meet the requirements of analysis and modeling for government projects
Data Analysis in AWS Cloud9 for Government
- Conducting exploratory data analysis using Python and R, aligned with government data policies
- Working with Pandas, NumPy, and data visualization libraries to support transparent reporting
- Performing statistical analysis and hypothesis testing in Cloud9 to inform policy decisions
Machine Learning Model Development for Government
- Building machine learning models using Scikit-learn and TensorFlow, adhering to government guidelines
- Training and evaluating models in AWS Cloud9 to ensure accuracy and reliability for government use
- Leveraging SageMaker with Cloud9 for scalable model development in government applications
Database Integration and Management for Government
- Integrating AWS RDS and Redshift with AWS Cloud9 to support government data infrastructure
- Querying large datasets using SQL and Python, ensuring compliance with government data governance
- Managing big data efficiently with AWS services for government operations
Model Deployment and Optimization for Government
- Deploying machine learning models using AWS Lambda to enhance government service delivery
- Using AWS CloudFormation to automate deployment processes in a secure and transparent manner
- Optimizing data pipelines for performance and cost-efficiency, aligned with government budget constraints
Collaborative Development and Security for Government
- Facilitating collaborative data science projects in Cloud9 to enhance interagency cooperation
- Utilizing Git for version control and project management, ensuring traceability and accountability
- Implementing security best practices to protect government data and models in AWS Cloud9
Summary and Next Steps for Government
Requirements
- Fundamental knowledge of data science principles
- Proficiency in Python programming
- Practical experience with cloud environments and AWS services
Audience
- Data scientists for government
- Data analysts
- Machine learning engineers
Testimonials (4)
All good, nothing to improve
Ievgen Vinchyk - GE Medical Systems Polska Sp. Z O.O.
Course - AWS Lambda for Developers
IOT applications
Palaniswamy Suresh Kumar - Makers' Academy
Course - Industrial Training IoT (Internet of Things) with Raspberry PI and AWS IoT Core 「4 Hours Remote」
It is great to have the course custom made to the key areas that I have highlighted in the pre-course questionnaire. This really helps to address the questions that I have with the subject matter and to align with my learning goals.
Winnie Chan - Statistics Canada
Course - Jupyter for Data Science Teams
The example and training material were sufficient and made it easy to understand what you are doing.
