From Data to Insights with Google Cloud Platform

Learn how to derive insights through data analysis and visualisation using the Google Cloud Platform on this three-day advanced data insights course.
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3 day course
Supporting material
Virtual, Private
Virtual Classroom
A convenient and interactive learning experience, that enables you to attend one of our courses from the comfort of your own home or anywhere you can log on. We offer Virtual Classroom on selected live classroom courses where this will appear as an option under the location drop down if available. These can also be booked as Private Virtual Classrooms for exclusive business sessions.
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

As a Google Cloud Partner, we’ll share best practice on how you can use the GCP tools efficiently to query and process petabytes of data in seconds.

Jellyfish has been selected by Google to facilitate the delivery of this three-day instructor led course. All of our trainers are experienced practitioners, so you can learn with total confidence.

The course features interactive scenarios and hands-on labs where you will explore, mine, load, visualise, and extract insights from diverse Google BigQuery datasets.

We’ll cover data loading, querying, schema modelling, optimising performance, query pricing, and data visualisation.

This From Data to Insights with Google Cloud Platform course is offered as a private training course and will run over three consecutive days and is part of the Google Cloud Platform Data Analyst Track. It can be delivered at our own training facilities in the Arenco Tower, any location of your choice or via Virtual Classroom.

Course overview
Who should attend:
This course is intended for Data Analysts, Business Analysts and Business Intelligence professionals. Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform will also benefit from attending this course.
Walk away with the ability to:
  • Derive insights from data using the analysis and visualisation tools on Google Cloud Platform
  • Interactively query datasets using Google BigQuery
  • Load, clean, and transform data at scale
  • Visualise data using Google Data Studio and other third-party platforms
  • Distinguish between exploratory and explanatory analytics and when to use each approach
  • Explore new datasets and uncover hidden insights quickly and effectively
  • Optimising data models and queries for price and performance
To get the most of out of this course, you should have basic proficiency with ANSI SQL and completed the Data Engineering on Google Cloud Platform course.
Course agenda
Module 1: Introduction Google Cloud Platform
  • Highlight Analytics Challenges Faced by Data Analysts
  • Compare Big Data On-Premises vs on the Cloud
  • Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud
  • Navigate Google Cloud Platform Project Basics
Module 2: Analysing Large Datasets with BigQuery
  • Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools
  • Demo: Analyse 10 Billion Records with Google BigQuery
  • Explore 9 Fundamental Google BigQuery Features
  • Compare GCP Tools for Analysts, Data Scientists, and Data Engineers
  • Lab: BigQuery Basics
Module 3: Exploring your Public Dataset with SQL
  • Compare Common Data Exploration Techniques
  • Learn How to Code High Quality Standard SQL
  • Explore Google BigQuery Public Datasets
  • Visualisation Preview: Google Data Studio
  • Lab: Explore your Ecommerce Dataset with SQL in Google BigQuery
Module 4: Cleaning and Transforming your Data with Cloud Dataprep
  • Examine the 5 Principles of Dataset Integrity
  • Characterise Dataset Shape and Skew
  • Clean and Transform Data using SQL
  • Clean and Transform Data using a new UI: Introducing Cloud Dataprep
  • Lab: Creating a Data Transformation Pipeline with Cloud Dataprep
Module 5: Visualising Insights and Creating Scheduled Queries
  • Overview of Data Visualisation Principles
  • Exploratory vs Explanatory Analysis Approaches
  • Demo: Google Data Studio UI
  • Connect Google Data Studio to Google BigQuery
  • Lab: How to Build a BI Dashboard Using Google Data Studio and BigQuery
Module 6: Storing and Ingesting new Datasets
  • Compare Permanent vs Temporary Tables
  • Save and Export Query Results
  • Performance Preview: Query Cache
  • Lab: Ingesting New Datasets into BigQuery
Module 7: Enriching your Data Warehouse with JOINs
  • Merge Historical Data Tables with UNION
  • Introduce Table Wildcards for Easy Merges
  • Review Data Schemas: Linking Data Across Multiple Tables
  • Walkthrough JOIN Examples and Pitfalls
  • Lab: Troubleshooting and Solving Data Join Pitfalls
Module 8: Partitioning your Queries and Tables for Advanced Insights
  • Review SQL Case Statements
  • Introduce Analytical Window Functions
  • Safeguard Data with One-Way Field Encryption
  • Discuss Effective Sub-query and CTE design
  • Compare SQL and Javascript UDFs
  • Lab: Creating Date-Partitioned Tables in BigQuery
Module 9: Designing Schemas that Scale: Arrays and Structs in BigQuery
  • Compare Google BigQuery vs Traditional RDBMS Data Architecture
  • Normalisation vs Denormalization: Performance Tradeoffs
  • Schema Review: The Good, The Bad, and The Ugly
  • Arrays and Nested Data in Google BigQuery
  • Lab: Querying Nested and Repeated Data
  • Lab: Schema Design for Performance: Arrays and Structs in BigQuery
Module 10: Optimising Queries for Performance
  • Walkthrough of a BigQuery Job
  • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
  • Optimise Queries for Cost
Module 11: Controlling Access with Data Security Best Practices
  • Data Security Best Practices
  • Controlling Access with Authorised Views
Module 12: Predicting Visitor Return Purchases with BigQuery ML
  • Intro to ML
  • Feature Selection
  • Model Types
  • Machine Learning in BigQuery
  • Lab: Predict Visitor Purchases with a Classification Model with BigQuery ML
Module 13: Deriving Insights from Unstructured Data using Machine Learning
  • Structured vs Unstructured ML
  • Prebuilt ML models
  • Lab: Extract, Analyze, and Translate Text from Images with the Cloud ML APIs
  • Lab: Training with Pre-built ML Models using Cloud Vision API and AutoML
Module 14: Completion
  • Summary and course wrap-up
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