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

What you will 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

Who this course is for

  • 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

Level

  • Introductory

Duration

  • 3 x 8 hour sessions

Prerequisites

  • Basic proficiency with ANSI SQL

Language

  • Delivered in English

Course TOPICS

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: Visualizing Insights and Creating Scheduled Queries

  • 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

  • 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

Ref: T-GCPBDI-B-02

Have questions?

No worries. Send us a quick message and we'll be happy to answer any questions you have.

Contact Instructor

© Copyright 2023. Axalon. All rights reserved.

Facebook site
LinkedIn profile