From Data to Insights with Google Cloud Training

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.
Course Details


3 days


Basic proficiency with ANSI SQL

Target Audience

  • 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

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
Course Outline
  • 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
  • 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
  • Exploring your Public Dataset with SQL
    • Common Data Exploration Techniques
    • Use SQL to Query Public Datasets
  • 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
  • Visualizing Insights and Creating Scheduled Queries
    • Data Visualization Principles
    • Common Data Visualization Pitfalls
    • Google Data Studio
  • Storing and Ingesting New Datasets
    • Permanent Versus Temporary Data Tables
    • Ingesting New Datasets
  • 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
  • Advanced Features and Partitioning your Queries and Tables for Advanced Insights
    • Advanced Functions (Statistical, Analytic, User-defined)
    • Date-Partitioned Tables
  • Designing Schemas that Scale: Arrays and Structs in BigQuery
    • BigQuery Versus Traditional Relational Data Architecture
    • ARRAY and STRUCT Syntax
    • BigQuery Architecture
  • Optimizing Queries for Performance
    • BigQuery Performance Pitfalls
    • Prevent Data Hotspots
    • Diagnose Performance Issues with the Query Explanation Map
  • Controlling Access with Data Security s
    • Hashing Columns
    • Authorized Views
    • IAM and BigQuery Dataset Roles
    • Access Pitfalls
  • 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
  • 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
Upcoming Course Dates
USD $2,700
Online Virtual Class
Date: Aug 7 - 9, 2024
Time: 9 AM - 5 PM ET
Partner Registration

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