From Data to Insights with Google Cloud

Name: From Data to Insights with Google Cloud
Start: 2023-10-23 00:00:00
Location: Virtual Classroom

Learn how to derive insights through data analysis and visualisation using Google Cloud on this three-day advanced data insights course.

3 day course

Supporting material

Google Cloud Partner of the Year

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.

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, Jellyfish has been chosen to facilitate this three-day course, where you’ll learn how to query and process data, perform data analysis, and work with insights from diverse Google BigQuery datasets.

Through a combination of lectures, demonstrations and hands-on exercises, we’ll show how to derive insights through data analysis and visualisation using Google Cloud. You’ll participate in interactive scenarios and hands-on labs where you’ll explore, mine, load, visualise, and extract insights from diverse Google BigQuery datasets.

The session will cover BigQuery fundamentals, including how to create a data transformation pipeline, build a BI dashboard, ingest new datasets, and design schemas at scale.

Our From Data to Insights with Google Cloud course is delivered via Virtual Classroom. We also offer it as a private training session that can be delivered virtually or at a location of your choice in the UK.

Course overview

Who should attend:

This course is intended for data analysts, business analysts and business intelligence professionals. Cloud data engineers partnering with data analysts to build scalable data solutions on Google Cloud will also benefit from attending.

What you'll learn:

By the end of this course, you will be able to:

Derive insights from data using the analysis and visualisation tools on Google Cloud
Load, clean, and transform data at scale with Google Cloud Dataprep
Explore and visualise data using Google Data 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 BQML

Prerequisites

To get the most out of this course, you should have basic proficiency with ANSI SQL.

Course agenda

Module 1: Introduction to Google Cloud

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 project basics

Module 2: Analysing Large Datasets with BigQuery

Walk through data analyst tasks, challenges, and introduce Google Cloud Data Tools
Demo: Analyse 10 billion records with Google BigQuery
Explore fundamental Google BigQuery features
Compare GC tools for analysts, data scientists, and data engineers

Module 3: Exploring your Public Dataset with SQL

Compare common data exploration techniques
Identify the key components of a basic SQL SELECT statement and common pitfalls
Discuss the basics of SQL functions and how they create calculated fields with input parameters
Explore Google BigQuery public datasets
Visualisation Preview: Google Data Studio

Module 4: Cleaning & Transforming your Data with Cloud Dataprep

Examine the five 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

Module 5: Visualising Insights & Creating Scheduled Queries

Overview of data visualisation principles
Common data visualisation pitfalls
Looker Studio

Module 6: Storing & Ingesting New Datasets

Compare permanent vs. temporary 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
Walkthrough JOIN Examples and Pitfalls

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

Advanced functions (statistical, analytics, user-defined)
Date-partitioned tables

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

Compare Google BigQuery vs. 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
Controlling access with authorised views
IAM and BigQuery dataset roles
Highlight key data access pitfalls

Module 12: Predicting Visitor Return Purchases with BigQuery ML

How does ML on structured data drive value?
Describe how customer LTV can be predicted with an ML model
Choose the right model type
Creating ML models with SQL

Module 13: Deriving Insights from Unstructured Data using Machine Learning

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

Upcoming courses

Virtual Classroom From Data to Insights with Google Cloud	Mon, Oct 23 2023 £1,599 ex VAT	ADD TO BASKET
Virtual Classroom From Data to Insights with Google Cloud	Mon, Mar 18 2024 £1,599 ex VAT	ADD TO BASKET
Virtual Classroom From Data to Insights with Google Cloud	Mon, Jul 22 2024 £1,599 ex VAT	ADD TO BASKET

Virtual Classroom