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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 two-day advanced data insights course.
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2 day course
Supporting material
Classroom, Virtual, Private
Classroom
Face to face, interactive classroom training run from our global training centres.
Virtual Classroom
A convenient and interactive learning experience, that enables you to attend on 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.
Private
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 two-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 available at our training centre in The Shard, London and is part of the Google Cloud Platform Data Analyst Track. This course will be run over two consecutive days. We also offer private training at a 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
Prerequisites
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 to Data on the 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
  • Lab: Getting started with Google Cloud Platform
Module 2: Big Data Tools Overview
  • 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: Exploring Datasets with Google BigQuery
Module 3: Exploring your Data 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: Troubleshoot Common SQL Errors
Module 4: Google BigQuery Pricing
  • Walkthrough of a BigQuery Job
  • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
  • Optimise Queries for Cost
  • Lab: Calculate Google BigQuery Pricing
Module 5: Cleaning and Transforming your Data
  • 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: Explore and Shape Data with Cloud Dataprep
Module 6: DStoring and Exporting Data
  • Compare Permanent vs Temporary Tables
  • Save and Export Query Results
  • Performance Preview: Query Cache
  • Lab: Creating new Permanent Tables
Module 7: Ingesting New Datasets into Google BigQuery
  • Query from External Data Sources
  • Avoid Data Ingesting Pitfalls
  • Ingest New Data into Permanent Tables
  • Discuss Streaming Inserts
  • Lab: Ingesting and Querying New Datasets
Module 8: Data Visualisation
  • Overview of Data Visualisation Principles
  • Exploratory vs Explanatory Analysis Approaches
  • Demo: Google Data Studio UI
  • Connect Google Data Studio to Google BigQuery
  • Lab: Exploring a Dataset in Google Data Studio
Module 9: Joining and Merging Datasets
  • 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: Join and Union Data from Multiple Tables
Module 10: Advanced Functions and Clauses
  • 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: Deriving Insights with Advanced SQL Functions
Module 11: Schema Design and Nested Data Structures
  • Compare Google BigQuery vs Traditional RDBMS Data Architecture
  • Normalisation vs Denormalisation: Performance Tradeoffs
  • Schema Review: The Good, The Bad, and The Ugly
  • Arrays and Nested Data in Google BigQuery
  • Lab: Querying Nested and Repeated Data
Module 12: More Visualisation with Google Data Studio
  • Create Case Statements and Calculated Fields
  • Avoid Performance Pitfalls with Cache considerations
  • Share Dashboards and Discuss Data Access considerations
Module 13: Optimising for Performance
  • Avoid Google BigQuery Performance Pitfalls
  • Prevent Hotspots in your Data
  • Diagnose Performance Issues with the Query Explanation map
  • Lab: Optimising and Troubleshooting Query Performance
Module 14: Advanced Insights
  • Introducing Cloud Datalab
  • Cloud Datalab Notebooks and Cells
  • Benefits of Cloud Datalab
Module 15: Data Access
  • Compare IAM and BigQuery Dataset Roles
  • Avoid Access Pitfalls
  • Review Members, Roles, Organisations, Account Administration, and Service Accounts
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