3 day course
Supporting material
Google Cloud Partner of the Year
Classroom, Virtual, Private
This course will teach you how to query and process data, perform data analysis that scales automatically as your data grows and explore, mine, load, visualise, and extract insights from diverse Google BigQuery datasets.
Through a combination of lectures, demonstrations and hands-on exercises, this course shows how to derive insights through data analysis and visualisation using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualise, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modelling, optimising performance, query pricing, data visualisation, and machine learning.
Jellyfish has recently been named a Google Cloud Specialisation Partner of the Year. This title recognises our commitment to provide world-leading Cloud-based Training solutions that help our clients succeed. All of our trainers are experienced practitioners, so you can learn with total confidence.
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
- 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 of out of this course, you should have basic proficiency with ANSI SQL.
Course agenda
Module 1: Introduction to 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
Module 2: Analysing Large Datasets with BigQuery
- 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: BigQuery Basics
Module 3: Exploring your Public Dataset 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: Explore your Ecommerce Dataset with SQL in Google BigQuery
Module 4: Cleaning and Transforming your Data with Cloud Dataprep
- 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: Creating a Data Transformation Pipeline with Cloud Dataprep
Module 5: Visualising Insights and Creating Scheduled Queries
- Overview of Data Visualisation Principles
- Exploratory vs Explanatory Analysis Approaches
- Demo: Google Data Studio UI
- Connect Google Data Studio to Google BigQuery
- Lab: How to Build a BI Dashboard Using Google Data Studio and BigQuery
Module 6: Storing and Ingesting New Datasets
- Compare Permanent vs Temporary Tables
- Save and Export Query Results
- Performance Preview: Query Cache
- Lab: Ingesting New Datasets into BigQuery
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
- Lab: Troubleshooting and Solving Data Join Pitfalls
Module 8: Partitioning your Queries and Tables for Advanced Insights
- Review SQL Case Statements
- Introduce Analytical Window Functions
- Safeguard Data with one-way Field Encryption
- Discuss Effective Sub-query and CET design
- Lab: Creating Date-Partitioned Tables in BigQuery
Module 9: Designing Schemas that Scale: Arrays and Structs in BigQuery
- 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
- Lab: Schema Design for Performance: Arrays and Structs in BigQuery
Module 10: Optimising Queries for Performance
- Walkthrough of a BigQuery Job
- Calculate BigQuery Pricing: Storage, Querying and Streaming Costs
- Optimise Queries for Cost
Module 11: Controlling Access with Data Security Best Practices
- Data Security Best Practices
- Controlling Access with Authorised Views
Module 12: Predicting Visitor Return Purchases with BigQuery ML
- Intro to ML
- Feature Selection
- Model Types
- Machine Learning in BigQuery
- Lab: Predict Visitor Purchases with a Classification Model with BigQuery ML
Module 13: Deriving Insights from Unstructured Data using Machine Learning
- Structured vs Unstructured ML
- Prebuilt ML models
- Lab: Extract, Analyse and Translated text from images with the Cloud ML APIs
- Lab: Training with pre-built ML models using Cloud Vision API and AutoML
Module 14: Completion
- Summary and course wrap-up
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