From Data to Insights with Google Cloud Platform (DIGCP)

• Analyzing and Visualizing Data with Looker (AVDL)
• Data Warehousing with BigQuery: Storage Design, Query Optimization, and Administration (DWBQ-SDQA)

Module 1: Introduction to Data on Google Cloud Platform

Topics:

• Analytics Challenges Faced by Data Analysts
• Big Data On-premise Versus on the Cloud
• Real-world Use Cases of Companies Transformed Through Analytics on the Cloud
• Google Cloud Project Basics

Objectives:

• Compare data infrastructure on-premises versus on Google Cloud.

Module 2: Analyzing Large Datasets with BigQuery

Topics:

• Data Analyst Tasks, Challenges, and Google Cloud Data Tools
• Fundamental BigQuery Features
• Google Cloud Tools for Analysts, Data Scientists, and Data Engineers

Objectives:

• 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 Data with SQL

Topics:

• Common Data Exploration Techniques
• Use SQL to Query Public Datasets

Objectives:

• 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

Topics:

• 5 Principles of Dataset Integrity
• Dataset Shape and Skew
• Clean and Transform Data using SQL
• Introducing Dataprep by Trifacta

Objectives:

• 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

Topics:

• Data Visualization Principles
• Common Data Visualization Pitfalls
• Looker Studio

Objectives:

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

Module 6: Storing and Ingesting New Datasheets

Topics:

• Permanent vs Temporary Data Tables
• Ingesting New Datasheets

Objectives:

• 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

Topics:

• Merge Historical Data Tables with UNION
• Introduce Table Wildcards for Easy Merges
• Review Data Schemas: Linking Data Across Multiple Tables
• JOIN Examples and Pitfalls

Objectives:

• 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

Topics:

• Advanced Functions (Statistical, Analytic, User-defined)
• Date-Partitioned Tables

Objectives:

• 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

Topics:

• BigQuery Versus Traditional Relational Data Architecture
• ARRAY and STRUCT Syntax
• BigQuery Architecture

Objectives:

• 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

Topics:

• BigQuery Performance Pitfalls
• Prevent Data Hotspots
• Diagnose Performance Issues with the Query Explanation Map

Objectives:

• 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

Topics:

• Hashing Columns
• Authorized Views
• IAM and BigQuery Dataset Roles
• Access Pitfalls

Objectives:

• 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

Topics:

• Machine Learning on Structured Data
• Scenario: Predicting Customer Lifetime Value
• Choosing the Right Model Type
• Creating ML models with SQL

Objectives:

• 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

Topics:

• ML Drives Business Value
• How does ML on Unstructured Data Work?
• Choosing the Right ML Approach
• Pre-built AI Building Blocks
• Customizing Pre-built Models with AutoML
• Building a Custom Model

Objectives:

• 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