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From Data to Insights with Google Cloud

SS Course: 62020

Course Overview

Explore ways to derive insights from data at scale using BigQuery, Google Clouds 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.                                                                  

Scheduled Classes

05/08/24 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
08/07/24 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

What You'll Learn

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


Viewing outline for:
BigQuery Performance Pitfalls
  • Prevent Data Hotspots
  • Diagnose Performance Issues with the Query Explanation Map
  • Hashing Columns
  • Authorized Views
  • IAM and BigQuery Dataset Roles
  • Access Pitfalls
  • Machine Learning on Structured Data
  • Scenario: Predicting Customer Lifetime Value
  • Choosing the Right Model Type
  • Creating ML models with SQL
  • 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
  • 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
  • Data Analyst Tasks, Challenges, and Google Cloud Data Tools
  • Fundamental BigQuery Features
  • Google Cloud Tools for Analysts, Data Scientists, and Data Engineers
  • Common Data Exploration Techniques
  • Use SQL to Query Public Datasets
  • 5 Principles of Dataset Integrity
  • Dataset Shape and Skew
  • Clean and Transform Data using SQL
  • Introducing Dataprep by Trifacta
  • Data Visualization Principles
  • Common Data Visualization Pitfalls
  • Google Data Studio
  • Permanent Versus Temporary Data Tables
  • Ingesting New Datasets
  • Merge Historical Data Tables with UNION
  • Introduce Table Wildcards for Easy Merges
  • Review Data Schemas: Linking Data Across Multiple Tables
  • JOIN Examples and Pitfalls
  • Advanced Functions (Statistical, Analytic, User-defined)
  • Date-Partitioned Tables
  • BigQuery Versus Traditional Relational Data Architecture
  • ARRAY and STRUCT Syntax
  • BigQuery Architecture


Basic proficiency with ANSI SQL

    Who Should Attend

    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

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