GOOGLE CLOUD
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 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
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
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
Introductory
3 x 8 hour sessions
Basic proficiency with ANSI SQL
Delivered in English
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
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
Ref: T-GCPBDI-B-02
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