Google Cloud Platform Big Data and Machine Learning Fundamentals
This 1 day course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.
This course teaches participants the following skills:
Knowledge of Google Cloud Platform products and services, particularly those related to data processing and machine learning
Knowledge of basic products and services related to computing and storage
Knowledge of Cloud SQL and Dataproc
Knowledge of Datalab and BigQuery
Knowledge of TensorFlow and Machine Learning APIs
Knowledge of Pub / Sub and Dataflow
To get the most out of this course, participants should have:
experience with a common query language such as SQL
experience with an ETL
data modeling experience
experience in machine learning and / or statistics
experience with programming in Python
This course is intended for the following participants:
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
The course includes presentations, demonstrations, and hands-on labs.
Module 1: Introducing Google Cloud Platform
Google Platform Fundamentals Overview.
Google Cloud Platform Big Data Products.
Module 2: Compute and Storage Fundamentals
CPUs on demand (Compute Engine).
A global filesystem (Cloud Storage).
Lab: Set up a Ingest-Transform-Publish data processing pipeline.
Module 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.
Module 4: Scaling Data Analysis
Fast random access.
Lab: Build machine learning dataset.
Module 5: Machine Learning
Machine Learning with TensorFlow.
Lab: Carry out ML with TensorFlow
Pre-built models for common needs.
Lab: Employ ML APIs.
Module 6: Data Processing Architectures
Message-oriented architectures with Pub/Sub.
Creating pipelines with Dataflow.
Reference architecture for real-time and batch data processing.
Module 7: Summary
Where to go from here
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Data Engineering on Google Cloud Platform
This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.
€2800 ex. VAT
From Data to Insights with Google Cloud Platform
Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL.
€2100 ex. VAT
Preparing for the Professional Data Engineer Examination
Preparing for the Professional Data Engineering Certification Exam. Elaborates on the exam. Rehearses useful skills including exam question reasoning and case comprehension. Tips. Review of topics from the Infrastructure curriculum.