In this lesson, we will learn how to:

1. Use AWS Sagemaker Studio to access datasets from S3 and perform data analysis functions using AWS tools
   

• how to create a Sagemaker studio and configure it.
• Access dataset from S3
• change the Kennel and instance type of studio
• clone git repositories as directors in notebook
• use Pandas from Jupiter lab instance

1. Perform data analysis and feature engineering with Data Wrangler

• Import data from S3
• visualize data
• transform Data
• Export the Results back to S3

1. Perform data analysis and feature engineering with Pandas in Sagemaker Studio

• Create DataFrame from data
• Saving data to CSV
• Summary statistics
• Histogram plotting
• correlation plotting

1. Label new data for a dataset with Sagemaker ground truth

• Creating annotation job
• Label private annotation job
• Export results to S3

Together, these skills will provide you with the necessary tools to analyze and create data for your machine learning models.