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Jan 1

From Data to Insights with Google Cloud

January 1 @ 8:00 AM - 5:00 PM AEST

Virtual Class Virtual Class
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From Data to Insights with Google Cloud

This instructor led live training teaches how to 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

What's Included?

Instructor Live Training

An instructor will answer your questions

OFFICIAL GOOGLE CLOUD CONTENT

Course content reflects the latest google cloud class

hands on labs

Real world hands on labs provided by Qwiklabs and supported by instructor

CertIficate of completion

Receive official certificate on completion of 80% of labs

Who's this course for?

Level

Language

Duration

Prerequisites

products

Course Content

Topics

  • 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

Objectives

  • Highlight analytics challenges faced by data analysts
  • Compare big data on-premise vs. in the cloud
  • Learn from real-world use cases of companies transformed through Analytics in the cloud
  • Navigate Google Cloud project basics

Topics

  • Data Analyst Tasks, Challenges, and Google Cloud Data Tools
  • Fundamental BigQuery Features
  • Google Cloud Tools for Analysts, Data Scientists, and Data Engineers

Objectives

  • Identify data analyst tasks, and challenges, and introduce Google Cloud data tools
  • Explore 9 fundamental BigQuery features
  • Compare big data technologies in a data architecture diagram
  • Compare the differences in roles and toolsets between data analysts, data scientists,
    and data engineers
  • Access the BigQuery web UI and explore a public dataset with basic SQL

Activities

  • 1 lab

Topics

  • Common Data Exploration Techniques’
  • Use SQL to Query Public Datasets

Objectives

  • Compare common data exploration techniques
  • Learn how to code high-quality standard SQL
  • Explore Google BigQuery public datasets

Activities

  • 1 lab

Topics

  • 5 Principles of Dataset Integrity
  • Dataset Shape and Skew
  • Clean and Transform Data using SQL
  • Introducing Dataprep by Trifacta

Objectives

  • Examine the 5 principles of dataset integrity
  • Characterize different dataset shapes and potential skew
  • Clean and transform data using SQL
  • Clean and transform data using Dataprep

Activities

  • 1 lab

Topics

  • Data Visualization Principles
  • Common Data Visualization Pitfalls
  • Google Data Studio

Objectives

  • Understand the visual perception principles of pre-attentive and post-attentive
    processing
  • Identify common data visualization pitfalls
  • Create dashboards and visualizations with Google Data Studio

Activities

  • 1 lab

Topics

  • Permanent Versus Temporary Data Tables
  • Ingesting New Datasets

Objectives

  • Differentiate between permanent and temporary data tables
  • Identify what types and formats of data BigQuery can ingest
  • Differentiate between native BigQuery table storage and external data source
    connections
  • Load new data into BigQuery

Activities

  • 1 lab

 

Topics

  • Merge Historical Data Tables with UNION
  • Introduce Table Wildcards for Easy Merges
  • Review Data Schemas: Linking Data Across Multiple Tables
  • JOIN Examples and Pitfalls

Objectives

  • Explain when to use UNIONs and when to use JOINs
  • Identify the key pitfalls when joining and merging datasets
  • Explain how union wildcards work and when to use them

Activities

  • 1 lab

Topics

  • Advanced Functions (Statistical, Analytic, User-defined)
  • Date-Partitioned Tables

Objectives

  • Identify the available statistical approximation functions and user-defined functions
  • Deconstruct an analytical window query and explain when to use RANK() and
    PARTITION
  • Explain when to use Common Table Expressions (WITH) to break apart complex
    queries

Activities

  • 1 lab

Topics

  • BigQuery Versus Traditional Relational Data Architecture
  • ARRAY and STRUCT Syntax
  • BigQuery Architecture

Objectives

  • Differentiate between BigQuery and traditional data architecture
  • Work with ARRAYs and STRUCTs as part of nested fields in data schemas

Activities

  • 1 lab

Topics

  • BigQuery Performance Pitfalls
  • Prevent Data Hotspots
  • Diagnose Performance Issues with the Query Explanation Map

Objectives

  • Avoid Google BigQuery performance pitfalls
  • Prevent hotspots in your data
  • Diagnose performance issues with the query explanation map

Topics

  • Hashing Columns
  • Authorized Views
  • IAM and BigQuery Dataset Roles
  • Access Pitfalls

Objectives

  • Use authorized views to limit row access
  • Compare IAM and BigQuery dataset roles
  • Avoid access pitfalls

Topics

  • Machine Learning on Structured Data
  • Scenario: Predicting Customer Lifetime Value
  • Choosing the Right Model Type
  • Creating ML models with SQL

Objectives

  • Explain how ML on structured data drives value
  • Describe how customer LTV can be predicted with an ML model
  • Choose the right model type for different structured data use cases
  • Create ML models with SQL

Activities

  • 1 lab

Topics

  • 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

Objectives

  • Discuss how ML is able to drive business value
  • Explain how ML on unstructured data works
  • Differentiate between pre-built ML models, custom models, and new models when
    considering an AI application strategy

Activities

  • 2 labs

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Ref: T-GCPBDI-B-02

Location

Online

Instructor

Axalon Academy
Email:
training@axalon.io
View Instructor Website

Other

Competencies
Beginner
Learning Path
Data Analyst
Event Type
Live Virtual Training Day