1 - Introducing Google Cloud Platform
Google Platform Fundamentals Overview.Google Cloud Platform Big Data Products.
2 - Compute and Storage Fundamentals
CPUs on demand (Compute Engine).A global filesystem (Cloud Storage).CloudShell.Lab: Set up a Ingest-Transform-Publish data processing pipeline.
3 - Data Analytics on the Cloud
Stepping-stones to the cloud.Cloud SQL: your SQL database on the cloud.Lab: Importing data into CloudSQL and running queries.Spark on Dataproc.Lab: Machine Learning Recommendations with Spark on Dataproc.
4 - Scaling Data Analysis
Fast random access.Datalab.BigQuery.Lab: Build machine learning dataset.
5 - Machine Learning
Machine Learning with TensorFlow.Lab: Carry out ML with TensorFlowPre-built models for common needs.Lab: Employ ML APIs.
6 - Data Processing Architectures
Message-oriented architectures with Pub/Sub.Creating pipelines with Dataflow.Reference architecture for real-time and batch data processing.
7 - Summary
Why GCP?Where to go from hereAdditional Resources
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it For?
This class is intended for the following:
Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.
Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.
Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists.
Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following:A common query language such as SQL Extract, transform, load activities Data modeling Machine learning and/or statistics Programming in Python