Google Cloud: Big Data And Machine Learning Fundamentals

• Data analysts getting started with Google Cloud Platform
• Data scientists getting started with Google Cloud Platform
• 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
  

In this course you will learn:

• Purpose and value of the key Big Data and Machine Learning products in the GoogleCloud Platform
• Use Cloud SQL and Cloud Dataproc to migrate existing MySQL andHadoop/Pig/Spark/Hive workloads to Google Cloud Platform
• Employ BigQuery and Cloud Datalab to carry out interactive data analysis
• Train and use a neural network using TensorFlow
• Employ ML APIs
• Choose between different data processing products on the Google Cloud Platform
  

1. Introducing Google Cloud Platform

• Google Platform Fundamentals Overview
• Google Cloud Platform Data Products and Technology
• Usage scenarios

2. Compute and Storage Fundamentals

• CPUs on demand (Compute Engine)
• A global filesystem (Cloud Storage)
• CloudShell

3. Data Analytics on the Cloud

• Stepping-stones to the cloud
• CloudSQL: your SQL database on the cloud
• Lab: Importing data into CloudSQL and running queries
• Spark on Dataproc

4. Scaling Data Analysis

• Fast random access
• Datalab
• BigQuery
• Machine Learning with TensorFlow
• Fully built models for common needs

5. Data Processing Architectures

• Message-oriented architectures with Pub/Sub
• Creating pipelines with Dataflow
• Reference architecture for real-time and batch data processing

6. Summary

• Why GCP?
• Where to go from here
• Additional Resources

Classroom Live Labs

Lab 1: Sign up for Google Cloud Platform

Lab 2: Set up a Ingest-Transform-Publish data processing pipeline

Lab 3: Machine Learning Recommendations with SparkML

Lab 4: Build machine learning dataset

Lab 5: Train and use neural network

Lab 6: Employ ML APIs

• Basic proficiency with common query language such as SQL
• Experience with data modeling, extract, transform, load activities
• Developing applications using a common programming language such Python
• Familiarity with Machine Learning and/or statistics