Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

This course teaches students the following skills:
Derive insights from data using the analysis and visualization tools on Google Cloud Platform
Interactively query datasets using Google BigQuery
Load, clean, and transform data at scale
Visualize data using Google Data Studio and other third-party platforms
Distinguish between exploratory and explanatory analytics and when to use each approach
Explore new datasets and uncover hidden insights quickly and effectively
Optimizing data models and queries for price and performance

1

  • Introduction to Data on the Google Cloud Platform

  • Highlight Analytics Challenges Faced by Data Analysts
    Compare Big Data On-Premises vs on the Cloud
    Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud
    Navigate Google Cloud Platform Project Basics
    Lab- Getting started with Google Cloud Platform
2

  • Big Data Tools Overview

  • Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools
    Demo- Analyze 10 Billion Records with Google BigQuery
    Explore 9 Fundamental Google BigQuery Features
    Compare GCP Tools for Analysts, Data Scientists, and Data Engineers
    Lab- Exploring Datasets with Google BigQuery
3

  • Exploring your Data with SQL

  • Compare Common Data Exploration Techniques
    Learn How to Code High Quality Standard SQL
    Explore Google BigQuery Public Datasets
    Visualization Preview- Google Data Studio
    Lab- Troubleshoot Common SQL Errors
4

  • Google BigQuery Pricing

  • Walkthrough of a BigQuery Job
    Calculate BigQuery Pricing- Storage, Querying, and Streaming Costs
    Optimize Queries for Cost
    Lab- Calculate Google BigQuery Pricing
5

  • Cleaning and Transforming your Data

  • Examine the 5 Principles of Dataset Integrity
    Characterize Dataset Shape and Skew
    Clean and Transform Data using SQL
    Clean and Transform Data using a new UI- Introducing Cloud Dataprep
    Lab- Explore and Shape Data with Cloud Dataprep
6

  • Storing and Exporting Data

  • Compare Permanent vs Temporary Tables
    Save and Export Query Results
    Performance Preview- Query Cache
    Lab- Creating new Permanent Tables
7

  • Ingesting New Datasets into Google BigQuery

  • Query from External Data Sources
    Avoid Data Ingesting Pitfalls
    Ingest New Data into Permanent Tables
    Discuss Streaming Inserts
    Lab- Ingesting and Querying New Datasets
8

  • Data Visualization

  • Overview of Data Visualization Principles
    Exploratory vs Explanatory Analysis Approaches
    Demo- Google Data Studio UI
    Connect Google Data Studio to Google BigQuery
    Lab- Exploring a Dataset in Google Data Studio
9

  • Joining and Merging Datasets

  • Merge Historical Data Tables with UNION
    Introduce Table Wildcards for Easy Merges
    Review Data Schemas- Linking Data Across Multiple Tables
    Walkthrough JOIN Examples and Pitfalls
    Lab- Join and Union Data from Multiple Tables
10

  • Advanced Functions and Clauses

  • Review SQL Case Statements
    Introduce Analytical Window Functions
    Safeguard Data with One-Way Field Encryption
    Discuss Effective Sub-query and CTE design
    Compare SQL and Javascript UDFs
    Lab- Deriving Insights with Advanced SQL Functions
11

  • Schema Design and Nested Data Structures

  • Compare Google BigQuery vs Traditional RDBMS Data Architecture
    Normalization vs Denormalization- Performance Tradeoffs
    Schema Review- The Good, The Bad, and The Ugly
    Arrays and Nested Data in Google BigQuery
    Lab- Querying Nested and Repeated Data
12

  • More Visualization with Google Data Studio

  • Create Case Statements and Calculated Fields
    Avoid Performance Pitfalls with Cache considerations
    Share Dashboards and Discuss Data Access considerations
13

  • Optimizing for Performance

  • Avoid Google BigQuery Performance Pitfalls
    Prevent Hotspots in your Data
    Diagnose Performance Issues with the Query Explanation map
    Lab- Optimizing and Troubleshooting Query Performance
14

  • Advanced Insights

  • Introducing Cloud Datalab
    Cloud Datalab Notebooks and Cells
    Benefits of Cloud Datalab
15

  • Data Access

  • Compare IAM and BigQuery Dataset Roles
    Avoid Access Pitfalls
    Review Members, Roles, Organizations, Account Administration, and Service Accounts

Audience

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 Platform will benefit from this course.

Language

English

Prerequisites

To get the most out of this course, participants should have: Basic proficiency with ANSI SQL

Course Info

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Cost: $ 1,900

Length: 3.0 days (24 hours)

Level:

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