GC Partner no outline H
7929  Reviews star_rate star_rate star_rate star_rate star_half

From Data to Insights with Google Cloud

Explore ways to derive insights from data at scale using BigQuery, Google Cloud’s serverless, highly scalable, and cost-effective cloud data warehouse. This course uses lectures, demos, and...

Read More
$2,700 USD GSA  $1,670.03
Course Code GCP-DI-3
Duration 3 days
Available Formats Classroom, Virtual

Explore ways to derive insights from data at scale using BigQuery, Google Cloud’s serverless, highly scalable, and cost-effective cloud data warehouse. This course uses lectures, demos, and hands-on labs to teach you the fundamentals of BigQuery, including how to create a data transformation pipeline, build a BI dashboard, ingest new datasets, and design schemas at scale.

Skills Gained

  • Derive insights from data using the analysis and visualization tools on Google Cloud
  • Load, clean, and transform data at scale with 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 BigQuery ML

Who Can Benefit

  • Data Analysts, Business Analysts, Business Intelligence professionals
  • Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud


  • Basic proficiency with ANSI SQL

Course Details

Course Outline

Module 1: Introduction to Data on Google Cloud

  • Analytics Challenges Faced by Data Analysts
  • Big Data On-premise Versus on the Cloud
  • Real-world Use Cases of Companies Transformed Through Analytics on the Cloud
  • Google Cloud Project Basics

Module 2: Analyzing Large Datasets with BigQuery

  • Data Analyst Tasks, Challenges, and Google Cloud Data Tools
  • Fundamental BigQuery Features
  • Google Cloud Tools for Analysts, Data Scientists, and Data Engineers

Module 3: Exploring your Public Dataset with SQL

  • Common Data Exploration Techniques
  • Use SQL to Query Public Datasets

Module 4: Cleaning and Transforming your Data with Dataprep

  • 5 Principles of Dataset Integrity
  • Dataset Shape and Skew
  • Clean and Transform Data using SQL
  • Introducing Dataprep by Trifacta

Module 5: Visualizing Insights and Creating Scheduled Queries

  • Data Visualization Principles
  • Common Data Visualization Pitfalls
  • Google Data Studio

Module 6: Storing and Ingesting New Datasets

  • Permanent Versus Temporary Data 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
  • JOIN Examples and Pitfalls

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

  • Advanced Functions (Statistical, Analytic, User-defined)
  • Date-Partitioned Tables

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

  • BigQuery Versus Traditional Relational Data Architecture
  • ARRAY and STRUCT Syntax
  • BigQuery Architecture

Module 10: Optimizing Queries for Performance

  • BigQuery Performance Pitfalls
  • Prevent Data Hotspots
  • Diagnose Performance Issues with the Query Explanation Map

Module 11: Controlling Access with Data Security s

  • Hashing Columns
  • Authorized Views
  • IAM and BigQuery Dataset Roles
  • Access Pitfalls

Module 12: Predicting Visitor Return Purchases with BigQuery ML

  • Machine Learning on Structured Data
  • Scenario: Predicting Customer Lifetime Value
  • Choosing the Right Model Type
  • Creating ML models with SQL

Module 13: Deriving Insights From Unstructured Data Using Machine Learning

  • ML Drives Business Value
  • 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
View Full Schedule