3 day course 
Partner of the Year 
Virtual, Private Book 2, Get 1 Free
Book two scheduled training sessions to be taken in 2024, and get a third one completely free! Simply add three courses to your basket, and the discount will be applied at check-out.
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 visualization using Google Cloud. You’ll participate in interactive scenarios and hands-on labs where you’ll explore, mine, load, visualize, 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 South Africa.
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 visualization tools on Google Cloud
- Load, clean, and transform data at scale with Google Cloud Dataprep
- Explore and visualize data using Google Data Studio
- Troubleshoot, optimize, 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: Analyze 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
- Visualization Preview: Google Data Studio
- Examine the five principles of Dataset Integrity
- Characterize dataset shape and skew
- Clean and transform data using SQL
- Clean and transform data using a new UI: Introducing Cloud Dataprep
- Overview of data visualization 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
- Walk through 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 authorized 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
- Customizing pre-built models with AutoML
- Building a custom model