Introduction to Data Science on the Google Cloud

Data Science on GCP Training Overview

This Data Science on the Google Cloud training course teaches engineers, data scientists, statisticians, and other quantitative professionals how to use the Google Cloud Platform for data science projects. Attendees learn how to use Jupyter notebooks with Google’s latest cloud tools for data science.

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

• Utilize Google Cloud Vertex AI (a managed Jupyter notebook service from Google Cloud) and take advantage of the features of the cloud for notebook computing.
  • Use the interface to run different notebook kernels and virtual machines in Vertex AI.
  • Explore Google Cloud Vertex AI sample notebooks and new use cases of data science on the cloud.
  • Use GitHub integration and Git via the graphical JupyterLab interface.
  • Write your own notebooks and use the Vertex AI notebook executor to schedule and parallelize running your notebooks as parameterized compute jobs.
• Use Google Cloud Public Datasets to get experience working with large datasets in the cloud (GB and TB scale).
  • Use the Google Cloud Shell to explore collections of files and buckets within Google Cloud Storage. Copy, sync, and move data to and from Vertex AI for analysis.
  • Practice by implementing and building upon steps described in one of the quick start guides for Google Cloud Public Datasets.
  • Write your own tutorial notebook explaining a use case in which you are interested.
• Explore and test Google Cloud Machine learning APIs.
  • Explore using Google Cloud Vision API to compare with the state of the art in computer vision.
  • Explore using Google Cloud Natural Language API to obtain valuable insights from text within documents.
  • Test and analyze the behavior of these machine learning services on your own data using Vertex AI.
  • Write your own analysis notebook. Explain your unique insights into the performance of the ML services and demonstrate this via testing on your own data.

Prerequisites

A background in data science or statistical programming (any language).

Outline

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Training Materials

All GCP for Data Science training students will receive courseware covering the topics in their class.

Software Requirements

A modern web browser and an Internet connection.