GCP-DI-3
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
Duration
3 days
Prerequisites
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
Scheduled
Date: Aug 7 - 9, 2024
Time: 9 AM - 5 PM ET
Partner Registration
The course you are registering for is being delivered by our sister company - ExitCertified. All logistics related to course delivery will be managed by the ExitCertified team. If you have a dedicated Web Age representative, please feel to reach out to them with any questions/concerns you may have.
You'll now be redirected to https://www.exitcertified.com to complete the enrollment process.
