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From Data to Insights with Google Cloud Platform

  • Length 3 days
  • Price  $2840 inc GST
Course overview
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Why study this course

Explore ways to derive insights from data at scale using BigQuery, Google Cloud’s serverless, highly scalable, and cost-effective cloud data warehouse.

This course uses lectures, demos, and hands-on labs to teach you the fundamentals of BigQuery, including how to create a data transformation pipeline, build a BI dashboard, ingest new datasets, and design schemas at scale.

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What you’ll learn

This course teaches participants the following skills:

  • Derive insights from data using the analysis and visualisation tools on Google Cloud

  • Load, clean, and transform data at scale with Dataprep

  • Explore and visualise data using Locker Studio

  • Troubleshoot, optimise, and write high performance queries

  • Practice with pre-built ML APIs for image and text understanding

  • Train classification and forecasting ML models using SQL with BigQuery ML

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Google Cloud at Lumify Work

Lumify Work is Australia's only national Google Cloud Authorised Training Partner. Get the skills needed to build, test, and deploy applications on this highly scalable infrastructure. Engineered to handle the most data-intensive work you can throw at it, Lumify Work can support you through training wherever you are in your Cloud adoption journey.

Who is the course for?

This course 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

Course subjects

Module 1: Introduction to Data on the Google Cloud

  • Analytics Challenges Faced by Data Analysts

  • Big Data On-Premise vs on the Cloud

  • Real-World Use Cases of Companies Transformed through Analytics on the Cloud

  • Google Cloud Project Basics

Module 2: Analysing Large Datasets with BigQuery

  • Data Analyst Tasks, Challenges, and Google Cloud Data Tools

  • Fundamental BigQuery Features

  • Google Cloud Tools for Analysts, Data Scientists, and Data Engineers

Module 3: Exploring your Public Dataset with SQL

  • Common Data Exploration Techniques

  • Use SQL to Query Public Datasets

Module 4: Cleaning and Transforming your Data with Dataprep

  • 5 Principles of Dataset Integrity

  • Dataset Shape and Skew

  • Clean and Transform Data using SQL

  • Introducing Dataprep by Trifacta

Module 5: Visualising Insights and Creating Scheduled Queries

  • Data Visualisation Principles

  • Common Data Visualisation Pitfalls

  • Looker Studio

Module 6: Storing and Ingesting New Datasets

  • Permanent vs Temporary Data Tables

  • Ingesting New Datasets

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

  • JOIN Examples and Pitfalls

Module 8: Advanced Features and Partitioning your Queries and Tables
for Advanced Insights

  • Advanced Functions (Statistical, Analytic, User-defined)

  • Date-Partitioned Tables

Module 9: Designing Schemas that Scale: Arrays and Structs in BigQuery

  • BigQuery Versus Traditional Relational Data Architecture

  • ARRAY and STRUCT Syntax

  • BigQuery Architecture

Module 10: Optimising Queries for Performance

  • BigQuery Performance Pitfalls

  • Prevent Data Hotspots

  • Diagnose Performance Issues with the Query Explanation Map

Module 11: Controlling Access with Data Security Best Practices

  • Hashing Columns

  • Authorised Views

  • IAM and BigQuery Dataset Roles

  • Access Pitfalls

Module 12: Predicting Visitor Return Purchases with BigQuery ML

  • Machine Learning on Structured Data

  • Scenario: Predicting Customer Lifetime Value

  • Choosing the Right Model Type

  • Creating ML models with SQL

Module 13: Deriving Insights From Unstructured Data Using Machine Learning

  • ML Drives Business Value

  • How does ML on Unstructured Data Work?

  • Choosing the Right ML Approach

  • Pre-built AI Building Blocks

  • Customising Pre-built Models with AutoML

  • Building a Custom Model


To get the most out of this course, participants should have:

  • Basic proficiency with ANSI SQL

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

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