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From Data to Insights with Google Cloud Platform

This three-day course teaches participants how to extract information through data analysis and visualisation using the Google Cloud Platform. 

The course features interactive scenarios and hands-on workshops where participants explore, mine, load, visualise and extract information from various BigQuery datasets. The course also covers data loading, querying, schema modelling, performance optimisation, query pricing and data visualisation.

  • Duration: 3 days
  • Format: Face-to-face or distance learning
  • Prerequisites: Good command of ANSI SQL.
  • Audience: Data analysts, business analysts, business intelligence experts, data engineers
  • Price: Please contact us
  • More information in our training catalogue

The course in detail

 

Module 1: Introduction to Google Cloud Platform

  • Discover the data analysis challenges faced by data analysts.
  • Compare on-premises Big Data versus Cloud.
  • Read about real-life cases where data analysis in the cloud has truly transformed a business.
  • Get to grips with the basics of navigating a Google Cloud Platform project.

 

Module 2: Analysing large datasets with BigQuery

  • Learn more about the tasks performed and the challenges faced by a data analyst, and gain an introduction to GCP’s data processing tools.
  • Demo: Analyse 10 billion entries with Google BigQuery.
  • Explore the 9 core features of Google BigQuery.
  • Compare GCP tools for analysts, data scientists and data engineers. Lab: Explore BigQuery basics.

 

Module 3: Browse a public dataset with SQL

  • Compare the most common data mining techniques.
  • Learn how to code with SQL to high quality standards.
  • Explore Google BigQuery datasets.
  • Previsualisation: Learn more about Google Data Studio.
  • Lab: Explore your e-commerce dataset with SQL in Google BigQuery.

 

Module 4: Clean and transform your data with Cloud Dataprep

  • Examine the 5 principles of dataset integrity.
  • Characterise the form and asymmetric data of a dataset.
  • Clean and transform data using SQL.
  • Clean and transform data using the new graphical interface:
    Introduction to Cloud Dataprep.
  • Lab: Create a data transformation pipeline with Cloud Dataprep.

 

Module 5: Visualise insights and create scheduled queries

  • Gain an overview of data visualisation principles.
  • Clarify exploratory versus explanatory analysis approaches.
  • Demo: Discover Google Data Studio UI.
  • Connect Google Data Studio to Google BigQuery.
  • Lab: Learn how to build a BI Dashboard using Google Data Studio and BigQuery.

 

Module 6: Storage and ingestion of new datasets

  • Compare permanent and temporary tables.
  • Save and export query results.
  • Performance overview: Learn about query caches.
  • Lab: Explore the ingestion of new datasets in BigQuery.

 

Module 7: Enhance your data warehouse with JOINs

  • Merge historical data tables with UNION.
  • Discover the presentation of table wildcards for easy merging.
  • Data schema review: data linkage between multiple tables.
  • Examples of JOINs and JOIN traps.
  • Lab: Troubleshooting and resolving JOIN issues.

 

Module 8: Partitioning your queries and tables for advanced information

  • Review SQL Case statements.
  • Watch a presentation of analytical window functions.
  • Protect your data with one-way field encryption.
  • Participate in a discussion on efficient sub-query design and CTE.
  • Compare SQL and JavaScript UDFs.
  • Lab: Create date-partitioned tables in BigQuery.

 

Module 9: Schema design to scale: tables and structures in BigQuery

  • Compare Google BigQuery to traditional RDBMS database architecture.
  • Learn about performance trade-offs with regards to standardisation vs. destandardisation.
  • Review schema: the good, the bad and the ugly.
  • Explore tables and nested data in Google BigQuery.
  • Lab: Explore nested and repeated data query.
  • Lab: Discover schema design for performance: tables and structures in BigQuery.

 

Module 10: Optimising queries for performance

  • Explore a BigQuery job.
  • Calculate BigQuery rates: storage, query and streaming costs.
  • Optimise queries for cost.

 

Module 11: Access control with data security best practices

  • Establish best practices in data security.
  • Enable access control with authorised views.

 

Module 12: Predicting visitors’ return purchases with BigQuery ML

  • Watch an introduction to ML.
  • Explore function selection.
  • Learn more about types of models.
  • Focus on machine learning in BigQuery.
  • Workshop: Predict visitor purchases with a classification model using BigQuery ML.

 

Module 13: Deriving information from unstructured data using machine learning

  • Learn about structured vs. unstructured ML.
  • Explore prebuilt ML models.
  • Workshop: Extract, analyse and translate text from images with Cloud ML APIs.
  • Workshop: Train with pre-defined ML models using the Cloud Vision API and AutoML.

 

Module 14: Conclusions

  • Summary and conclusion of the course

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