3 day course 
Partner of the Year 
Virtual, Private 
Certificate of Attendance 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:
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
- 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
- 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
- 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
- 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
- Overview of data visualisation principles
- Common data visualisation pitfalls
- Looker Studio
- Compare permanent vs. temporary tables
- Ingesting new 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
- Advanced functions (statistical, analytics, user-defined)
- Date-partitioned tables
- Compare Google BigQuery vs. Traditional Relational data architecture
- ARRAY and STRUCT syntax
- BigQuery architecture
- BigQuery performance pitfalls
- Prevent data hotspots
- Diagnose performance issues with the query explanation map
- Hashing columns
- Controlling access with authorised views
- IAM and BigQuery dataset roles
- Highlight key data access pitfalls
- 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
- 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