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From Data to Insights with 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



  • Basic proficiency with ANSI SQL


Course Outline

  • Module 1: Introduction to Data on Google Cloud
    • 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
  • 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 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
  • Module 3: Exploring your Public Dataset with SQL
    • Compare common data exploration techniques
    • Learn how to code high-quality standard SQL
    • Explore Google BigQuery public datasets
  • Module 4: Cleaning and Transformation your Data with Dataprep
    • 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
  • Module 5: Visualizing Insights and Creating Scheduled Queries
    • 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
  • 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
    • Explain how union wildcards work and when to use them
  • Module 8: Advanced Features and Partitioning your Queries and Tables for Advanced Insights
    • 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
  • 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
    • Avoid Google BigQuery performance pitfalls
    • Prevent hotspots in your data
    • Diagnose performance issues with the query explanation map
  • Module 11: Controlling Access with Data Security s
    • Use authorized views to limit row access
    • Compare IAM and BigQuery dataset roles
    • Avoid access pitfalls
  • 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


Jadwal Training

Tanggal Pukul Biaya (per pax; belum termasuk VAT 10%) Trainer Venue Daftar
TBA TBA Rp 9 juta Satria Yuda Utama Online TBA
TBA TBA Rp 9 juta Satria Yuda Utama Online TBA
TBA TBA Rp 9 juta Satria Yuda Utama Online TBA
TBA TBA Rp 9 juta Satria Yuda Utama Online TBA
TBA TBA Rp 9 juta Satria Yuda Utama Online TBA

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