Introduction to Data Science on the AWS Platform

Data Science with AWS Training Overview

Scale your data science workloads on Amazon Web Services to take advantage of on-demand delivery of compute power, database services, storage, applications, and IT resources, as well as tools that are unique to the AWS platform.

This in-person or online Data Science on the AWS Platform training course teaches engineers, data scientists, statisticians, and other quantitative professionals how to use AWS (Amazon Web Services) with Jupyter notebooks for data science to create scalable data analytics solutions.

Did you miss our live webinar? You can still view the AWS and Data Science with Python webinar recording.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, instructor-led training from one of our partners.

Objectives

• Use AWS SageMaker (a managed Jupyter notebook service from AWS)
  • Use the interface to run different notebook kernels and virtual machines in SageMaker
  • Explore AWS sample notebooks and new use cases of data science on the cloud
  • Use the GitHub integration and Git via the graphical JupyterLab interface
  • Write notebooks and use the SageMaker Papermill integration to schedule and parallelize running notebooks as parameterized compute jobs
• Use Open Datasets on AWS
  • Gain experience working with large datasets in the cloud (GB and TB scale)
  • Use the AWS CLI to explore collections of files and buckets within Amazon S3
  • Copy, sync, and move data to and from SageMaker for analysis
  • Implement and build upon steps described in tutorial notebooks from the Registry of Open Data
  • Write a tutorial notebook explaining a use case you are interested in
• Explore and test AWS Machine learning APIs
  • Explore using Amazon Rekognition,the state of the art in computer vision
  • Explore using Amazon Comprehend to obtain valuable insights from text within documents
  • Test and analyze the behavior of these machine learning services on your own data using AWS SageMaker
  • Write an analysis notebook
  • Explain unique insights into the performance of the ML services and demonstrate by testing on data

Prerequisites

All students must have experience with data science or statistical programming (any language).

Outline

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Training Materials

All AWS for Data Science training students will receive comprehensive courseware.

Software Requirements

A modern web browser and an Internet connection.