From Data to Insights with Google Cloud Platform (DIGCP)

Tijdsduur
Startdatum en plaats
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Startdata en plaatsen

Utrecht
26 mei. 2020 tot 28 mei. 2020

Beschrijving

Course Content

  • Module 1: Introduction to Data on the Google Cloud Platform
  • Module 2: Big Data Tools Overview
  • Module 3: Exploring your Data with SQL
  • Module 4: Google BigQuery Pricing
  • Module 5: Cleaning and Transforming your Data
  • Module 6: Storing and Exporting Data
  • Module 7: Ingesting New Datasets into Google BigQuery
  • Module 8: Data Visualization
  • Module 9: Joining and Merging Datasets
  • Module 10: Advanced Functions and Clauses
  • Module 11: Schema Design and Nested Data Structures
  • Module 12: More Visualization with Google Data Studio
  • Module 13: Optimizing for Performance
  • Module 14: Data Access
  • Module 15: Notebooks in the Cloud
  • Module 16: How Google does Machine Learning
  • Module 17: Applying Mach…

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Course Content

  • Module 1: Introduction to Data on the Google Cloud Platform
  • Module 2: Big Data Tools Overview
  • Module 3: Exploring your Data with SQL
  • Module 4: Google BigQuery Pricing
  • Module 5: Cleaning and Transforming your Data
  • Module 6: Storing and Exporting Data
  • Module 7: Ingesting New Datasets into Google BigQuery
  • Module 8: Data Visualization
  • Module 9: Joining and Merging Datasets
  • Module 10: Advanced Functions and Clauses
  • Module 11: Schema Design and Nested Data Structures
  • Module 12: More Visualization with Google Data Studio
  • Module 13: Optimizing for Performance
  • Module 14: Data Access
  • Module 15: Notebooks in the Cloud
  • Module 16: How Google does Machine Learning
  • Module 17: Applying Machine Learning to your Datasets (BQML)

Prerequisites

To get the most out of this course, participants should have:

  • Basic proficiency with ANSI SQL

Who Should Attend

This class is intended for the following:

  • 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

Gedetailleerde cursusinhoud

Module 1: Introduction to Data on the Google Cloud Platform

  • Highlight Analytics Challenges Faced by Data Analysts
  • Compare Big Data On-Premise 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

Module 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

Module 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

Module 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

Module 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

Module 6: Storing and Exporting Data

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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 14: Data Access

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

Module 15: Notebooks in the Cloud

  • Cloud Datalab
  • Compute Engine and Cloud Storage
  • Lab: Rent-a-VM to process earthquakes data
  • Data Analysis with BigQuery

Module 16: How Google does Machine Learning

  • Introduction to Machine Learning for analysts
  • Practice with Pretrained ML APIs for image and text understanding
  • Lab: Pretrained ML APIs

Module 17: Applying Machine Learning to your Datasets (BQML)

  • Building Machine Learning datasets and analyzing features
  • Creating classification and forecasting models with BQML
  • Lab: Predict Visitor Purchases with a Classification Model in BQML
  • Lab: Predict Taxi Fare with a BigQuery ML Forecasting Model

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