From Data to Insights with Google Cloud Platform

Name: From Data to Insights with Google Cloud Platform
Price: 1599.00 GBP

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

3 day course

Supporting material

Virtual, Private

Virtual Classroom

A convenient and interactive learning experience, that enables you to attend on 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, we’ll share best practice on how you can use the GCP tools efficiently to query and process petabytes of data in seconds.

Jellyfish has been selected by Google to facilitate the delivery of this three-day instructor led course. All of our trainers are experienced practitioners, so you can learn with total confidence.

The course features interactive scenarios and hands-on labs where you will explore, mine, load, visualise, and extract insights from diverse Google BigQuery datasets.

We’ll cover data loading, querying, schema modelling, optimising performance, query pricing, and data visualisation.

This From Data to Insights with Google Cloud Platform course is available at our training centre in The Shard, London and is part of the Google Cloud Platform Data Analyst Track. This course will be run over three consecutive days. We also offer private training at a location of your choice or via Virtual Classroom.

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
Interactively query datasets using Google BigQuery
Load, clean, and transform data at scale
Visualise data using Google Data Studio and other third-party platforms
Distinguish between exploratory and explanatory analytics and when to use each approach
Explore new datasets and uncover hidden insights quickly and effectively
Optimising data models and queries for price and performance

Prerequisites

To get the most of out of this course, you should have basic proficiency with ANSI SQL and completed the Data Engineering on Google Cloud Platform course.

Course agenda

Module 1: Introduction 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 CTE design
Compare SQL and Javascript UDFs
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 Denormalization: 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, Analyze, and Translate 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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