Google Cloud

From Data to Insights with Google Cloud

21 hours
1380,00 €
Classroom or Live Virtual Class
Classroom or Live Virtual Class

Note: The prices indicated below do not include 21% VAT.

18 Mar 2024 - 20 Mar 2024   |  

From Data to Insights with Google Cloud

21 h | 1380 € | Live Virtual Class | Spanish
Monday - Tuesday - Wednesday (09:00h - 17:00h)
Calendario de sesiones

Description

TASTE OF 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.

 

Audience and prerequisites

This class is intended for the following job roles:

  • 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 

Prerequisites:

To get the most out of this course, participants should have:

  • Basic proficiency with ANSI SQL

Objectives

This course teaches participants the following skills:

  • 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 Looker 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

Topics

Module 1:  Introduction to Data on Google Cloud

  • Compare data infrastructure on-premises versus on Google Cloud

Module 2: Analyzing Large Datasets with BigQuery

  • Identify data analyst tasks, and challenges, and introduce Google Cloud data tools
  • Explore 9 fundamental BigQuery features
  • 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 

Module 3: Exploring your Public Dataset with SQL

  • Compare common data exploration techniques
  • Identify the key components of a basic SQL SELECT statement and common pitfalls
  • Discuss the basics of SQL functions and how they create calculated fields with input parameters
  • Explore BigQuery public datasets
  • Troubleshoot dataset quality issues by analyzing duplicate records with SQL in the BigQuery Web UI

Module 4: Cleaning and Transforming your Data with Dataprep

  • Characterize different dataset shapes and potential skew
  • Clean and transform data using SQL
  • Clean and transform data using Dataprep 

Module 5: Visualizing Insights and Creating Scheduled Queries

  • Compare data visualizations and make recommendations for improvement
  • Create dashboards and visualizations with Looker Studio 

Module 6: Storing and Ingesting New Datasets

  • 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 

Module 7: Enriching your Data Warehouse with JOINs

  • Explain when to use UNIONs and when to use JOINs
  • Identify the key pitfalls when joining and merging datasets
  • Differentiate between join types visually
  • Explain how union wildcards work and when to use them
  • Write SQL JOINs and UNIONs against a dataset in the BigQuery web UI 

Module 8: Advanced Features and Partitioning your Queries and Tables for Advanced Insights

  • Identify the available statistical approximation functions and user-defined functions
  • Apply large-scale record estimation with approximate aggregation 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

Module 9: Designing Schemas that Scale: Arrays and Structs in BigQuery

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

Module 10: Optimizing Queries for Performance

  • Identify BigQuery performance pitfalls
  • Discuss the Query Explanation map and how to interpret MAX and AVG processing times per stage
  • Describe how to analyze and troubleshoot broken queries

Module 11: Controlling Access with Data Security Best Practices

  • Review data access roles within Google Cloud and BigQuery
  • Highlight key data access pitfalls and how to avoid them

Module 12: Predicting Visitor Return Purchases with BigQuery ML

  • 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 

Module 13: Deriving Insights From Unstructured Data Using Machine Learning

  • 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

Open calls

Note: The prices indicated below do not include 21% VAT.

18 Mar 2024 - 20 Mar 2024   |  

From Data to Insights with Google Cloud

21 h | 1380 € | Live Virtual Class | Spanish
Monday - Tuesday - Wednesday (09:00h - 17:00h)
Calendario de sesiones