From Data to Insights with Google Cloud Platform Nov 09/10/11

g-company is committed to helping organizations transform and become more flexible. We believe in the power of Google Cloud and Salesforce and developed a set of solutions that can be implemented independently, at your pace and in a sequence that fits your path to a modern architecture.

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Explore ways to derive insights from data at scale using BigQuery.

Check out all our training and other events on our <a href="https://g.company/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">website</a>!This course uses lectures, demos, and hands-on labs to teach you the fundamentals of BigQuery, including how to create a data transformation pipeline, build a BI dashboard, ingest new datasets, and design schemas at scale.The training will be given in 3 days.<h3>Course overview</h3>Target audience:<ul><li>This course is intended for the following participants:</li><li>Data analysts, business analysts, business intelligence professionals</li></ul>Objectives:<ul><li>Derive insights from data using the analysis and visualization tools on Google Cloud Platform</li><li>Load, clean, and transform data at scale with Google Cloud Dataprep</li><li>Explore and Visualize data using Google Data Studio</li><li>Troubleshoot, optimize, and write high performance queries </li><li>Practice with pre-built ML APIs for image and text understanding</li><li>Train classification and forecasting ML models using SQL with BQML</li></ul>Prerequisites:<ul><li>To get the most of out of this course, participants should have:</li><li>Basic proficiency with ANSI SQL</li></ul><h3>Content</h3>Module 1: Introduction to Data on the Google Cloud Platform<ul><li>Highlight Analytics Challenges Faced by Data Analysts.</li><li>Compare Big Data On-Premise vs on the Cloud.</li><li>Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud.</li><li>Navigate Google Cloud Platform Project Basics.</li><li>Lab: Getting started with Google Cloud Platform.</li></ul>Module 2: Big Data Tools Overview<ul><li>Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools.</li><li>Demo: Analyze 10 Billion Records with Google BigQuery.</li><li>Explore 9 Fundamental Google BigQuery Features.</li><li>Compare GCP Tools for Analysts, Data Scientists, and Data Engineers.</li><li>Lab: Exploring Datasets with Google BigQuery.</li></ul>Module 3: Exploring your Data with SQL<ul><li>Compare Common Data Exploration Techniques.</li><li>Learn How to Code High Quality Standard SQL.</li><li>Explore Google BigQuery Public Datasets.</li><li>Visualization Preview: Google Data Studio.</li><li>Lab: Troubleshoot Common SQL Errors.</li></ul>Module 4: Google BigQuery Pricing<ul><li>Walkthrough of a BigQuery Job.</li><li>Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs.</li><li>Optimize Queries for Cost.</li><li>Lab: Calculate Google BigQuery Pricing.</li></ul>Module 5: Cleaning and Transforming your Data<ul><li>Examine the 5 Principles of Dataset Integrity.</li><li>Characterize Dataset Shape and Skew.</li><li>Clean and Transform Data using SQL.</li><li>Clean and Transform Data using a new UI: Introducing Cloud Dataprep.</li><li>Lab: Explore and Shape Data with Cloud Dataprep</li></ul>Module 6: Storing and Exporting Data<ul><li>Compare Permanent vs Temporary Tables.</li><li>Save and Export Query Results.</li><li>Performance Preview: Query Cache.</li><li>Lab: Creating new Permanent Tables.</li></ul>Module 7: Ingesting New Datasets into Google BigQuery<ul><li>Query from External Data Sources.</li><li>Avoid Data Ingesting Pitfalls.</li><li>Ingest New Data into Permanent Tables.</li><li>Discuss Streaming Inserts.</li><li>Lab: Ingesting and Querying New Datasets.</li></ul>Module 8: Data Visualization<ul><li>Overview of Data Visualization Principles.</li><li>Exploratory vs Explanatory Analysis Approaches.</li><li>Demo: Google Data Studio UI.</li><li>Connect Google Data Studio to Google BigQuery.</li><li>Lab: Exploring a Dataset in Google Data Studio.</li></ul>Module 9: Joining and Merging Datasets<ul><li>Merge Historical Data Tables with UNION.</li><li>Introduce Table Wildcards for Easy Merges.</li><li>Review Data Schemas: Linking Data Across Multiple Tables.</li><li>Walkthrough JOIN Examples and Pitfalls.</li><li>Lab: Join and Union Data from Multiple Tables.</li></ul>Module 10: Advanced Functions and Clauses<ul><li>Review SQL Case Statements.</li><li>Introduce Analytical Window Functions.</li><li>Safeguard Data with One-Way Field Encryption.</li><li>Discuss Effective Sub-query and CTE design.</li><li>Compare SQL and Javascript UDFs.</li><li>Lab: Deriving Insights with Advanced SQL Functions.</li></ul>Module 11: Schema Design and Nested Data Structures<ul><li>Compare Google BigQuery vs Traditional RDBMS Data Architecture.</li><li>Normalization vs Denormalization: Performance Tradeoffs.</li><li>Schema Review: The Good, The Bad, and The Ugly.</li><li>Arrays and Nested Data in Google BigQuery.</li><li>Lab: Querying Nested and Repeated Data.</li></ul>Module 12: More Visualization with Google Data Studio<ul><li>Create Case Statements and Calculated Fields.</li><li>Avoid Performance Pitfalls with Cache considerations.</li><li>Share Dashboards and Discuss Data Access considerations.</li></ul>Module 13: Optimizing for Performance<ul><li>Avoid Google BigQuery Performance Pitfalls.</li><li>Prevent Hotspots in your Data.</li><li>Diagnose Performance Issues with the Query Explanation map.</li><li>Lab: Optimizing and Troubleshooting Query Performance.</li></ul>Module 14: Data Access<ul><li>Compare IAM and BigQuery Dataset Roles.</li><li>Avoid Access Pitfalls.</li><li>Review Members, Roles, Organizations, Account Administration, and Service Accounts.</li></ul>Module 15: Notebooks in the Cloud<ul><li>Cloud Datalab.</li><li>Compute Engine and Cloud Storage.</li><li>Lab: Rent-a-VM to process earthquakes data.</li><li>Data Analysis with BigQuery.</li></ul>Module 16: How Google does Machine Learning<ul><li>Introduction to Machine Learning for analysts.</li><li>Practice with Pretrained ML APIs for image and text understanding.</li><li>Lab: Pretrained ML APIs.</li></ul>Module 17: Applying Machine Learning to your Datasets (BQML)<ul><li>Building Machine Learning datasets and analyzing features.</li><li>Creating classification and forecasting models with BQML.</li><li>Lab: Predict Visitor Purchases with a Classification Model in BQML.</li><li>Lab: Predict Taxi Fare with a BigQuery ML Forecasting Model.</li></ul>

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From Data to Insights with Google Cloud Platform Nov 09/10/11

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From Data to Insights with Google Cloud Platform Nov 09/10/11

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