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

• 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

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 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

Activities

• 1 lab

Topics

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

Objectives

• Compare common data exploration techniques
• Learn how to code high-quality standard SQL
• Explore Google BigQuery public datasets

Activities

• 1 lab

Topics

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

Objectives

• 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

Activities

• 1 lab

Topics

• Data Visualization Principles
• Common Data Visualization Pitfalls
• Google Data Studio

Objectives

• 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

Activities

• 1 lab

Topics

• Permanent Versus Temporary Data Tables
• Ingesting New Datasets

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

Activities

• 1 lab

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
• Explain how union wildcards work and when to use them

Activities

• 1 lab

Topics

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

Objectives

• 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

Activities

• 1 lab

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

Activities

• 1 lab

Topics

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

Objectives

• Avoid Google BigQuery performance pitfalls
• Prevent hotspots in your data
• Diagnose performance issues with the query explanation map

Topics

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

Objectives

• Use authorized views to limit row access
• Compare IAM and BigQuery dataset roles
• Avoid access pitfalls

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

Activities

• 1 lab

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

Activities

• 2 labs