From Data to Insights with Google Cloud

​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 nine 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 pitalls.
  Discuss the basics of SQL functions and how they create calculated felds 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 pitalls 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 Paritioning your Queries and Tables for​ Advanced Insights

• ​Identify the available statistical approximation functions and userdefned 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 apar​t​ 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 par of nested f​ields in data schemas.

Module 10​: Optimizing Queries for Perormance

• ​Identify BigQuery perormance pitalls.

• 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 pitalls 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.

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

• 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:

• Basic proficiency with ANSI SQL