AWS Cloud9 for Data Science Training Course
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.
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
Open Training Courses require 5+ participants.
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Testimonials (3)
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」
Subject presentation knowledge timing
Aly Saleh - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)
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