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From Data to Insights com Google Cloud

Este treinamento de três dias ensina aos participantes do curso como obter insights por meio da análise e visualização de dados usando o Google Cloud Platform. O curso apresenta cenários interativos e laboratórios práticos, onde os participantes exploram, exploram, carregam, visualizam e extraem informações de diversos conjuntos de dados do Google BigQuery. O curso também abrange carregamento de dados, consultas, modelagem de esquemas, otimização de desempenho, preços de consultas e visualização de dados.

Público-Alvo

Esta aula destina-se ao seguinte público:
  • Data Analysts,
  • Business Analysts,
  • Business Intelligence professionals,
  • Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on GCP.

Pré-requisitos

Para aproveitar ao máximo este curso, os participantes precisam atender aos seguintes critérios:
  • Proficiência básica em linguagem de consulta comum, como SQL.

Duração

3 dias

Investimento

Consulte o valor atualizado e próximas datas para turmas abertas em nossa página de inscrições. Caso tenha interesse em uma turma fechada para sua empresa, entre em contato conosco.
Dependências de outros cursos e certificações com o curso de From Data to Insights with Google Cloud Platform
Dependências de outros cursos e certificações com o curso de From Data to Insights with Google Cloud Platform

Resumo do curso

O curso inclui apresentações, demonstrações e laboratórios práticos.
  • Highlight Analytics Challenges Faced by Data Analysts
  • Compare Big Data On-Premises vs on the Cloud
  • Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud
  • Navigate Google Cloud Platform Project Basics
  • Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools
  • Demo: Analyze 10 Billion Records with Google BigQuery
  • Explore 9 Fundamental Google BigQuery Features
  • Compare GCP Tools for Analysts, Data Scientists, and Data Engineers
  • Lab: BigQuery Basics
  • Compare Common Data Exploration Techniques
  • Learn How to Code High Quality Standard SQL
  • Explore Google BigQuery Public Datasets
  • Visualization Preview: Google Data Studio
  • Lab: Explore your Ecommerce Dataset with SQL in Google BigQuery
  • Examine the 5 Principles of Dataset Integrity
  • Characterize Dataset Shape and Skew
  • Clean and Transform Data using SQL
  • Clean and Transform Data using a new UI: Introducing Cloud Dataprep
  • Lab: Creating a Data Transformation Pipeline with Cloud Dataprep
  • Overview of Data Visualization Principles
  • Exploratory vs Explanatory Analysis Approaches
  • Demo: Google Data Studio UI
  • Connect Google Data Studio to Google BigQuery
  • Lab: How to Build a BI Dashboard Using Google Data Studio and BigQuery
  • Compare Permanent vs Temporary Tables
  • Save and Export Query Results
  • Performance Preview: Query Cache
  • Lab: Ingesting New Datasets into BigQuery
  • Merge Historical Data Tables with UNION
  • Introduce Table Wildcards for Easy Merges
  • Review Data Schemas: Linking Data Across Multiple Tables
  • Walkthrough JOIN Examples and Pitfalls
  • Lab: Troubleshooting and Solving Data Join Pitfalls
  • Review SQL Case Statements
  • Introduce Analytical Window Functions
  • Safeguard Data with One-Way Field Encryption
  • Discuss Effective Sub-query and CTE design
  • Compare SQL and Javascript UDFs
  • Lab: Creating Date-Partitioned Tables in BigQuery
  • Compare Google BigQuery vs Traditional RDBMS Data Architecture
  • Normalization vs Denormalization: Performance Tradeoffs
  • Schema Review: The Good, The Bad, and The Ugly
  • Arrays and Nested Data in Google BigQuery
  • Lab: Querying Nested and Repeated Data
  • Lab: Schema Design for Performance: Arrays and Structs in BigQuery
  • Walkthrough of a BigQuery Job
  • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
  • Optimize Queries for Cost
  • Data Security Best Practices
  • Controlling Access with Authorized Views
  • Structured vs Unstructured ML
  • Prebuilt ML models
  • Lab: Extract, Analyze, and Translate Text from Images with the Cloud ML APIs
  • Lab: Training with Pre-built ML Models using Cloud Vision API and AutoML
  • Summary and course wrap-up