Course overview
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. This specialization is intended for the following participants: 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 To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL.
Learning outcomes
This course teaches participants the following skills:
- Obtenir des informations à partir de données à l’aide des outils d’analyse et de visualisation de Google Cloud Platform
- Analyser des données à l’échelle avec BigQuery
- Charger, nettoyer et transformer les données à grande échelle à l’aide de Google Cloud Dataprep
- Explorer et visualiser les données à l’aide de Google Data Studio
- Optimiser et écrire des requêtes hautes performances et résoudre les problèmes associés
- Dériver des informations à partir de données à l’aide du Machine Learning
Prerequisites
To get the most out of this course, participants should have:
- Une certaine maîtrise d’ANSI SQL.
Target audience
This course is intended for the following participants:
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
Course Outline
The course includes presentations, demonstrations, and hands-on labs.
Module 1: Intro to 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
Module 2: Analyzing Large Datasets with BigQuery
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: BigQuery Basics
Module 3: Exploring your Public Dataset 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: Explore your Ecommerce Dataset with SQL in Google BigQuery
Module 4: Cleaning and Transforming your Data with Cloud Dataprep
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: Creating a Data Transformation Pipeline with Cloud Dataprep
Module 5: Visualizing Insights and Creating Scheduled Queries
Overview of Data Visualization Principles Exploratory vs Explanatory Analysis Approaches Demo: Google Data Studio UI Connect Google Data Studio to Google BigQuery Lab: How to Build a BI Dashboard Using Google Data Studio and BigQuery
Module 6: Storing and Ingesting new Datasets
Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Ingesting New Datasets into BigQuery
Module 7: Enriching your Data Warehouse with JOINs
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: Troubleshooting and Solving Data Join Pitfalls
Module 8: Partitioning your Queries and Tables for Advanced Insights
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: Creating Date-Partitioned Tables in BigQuery
Module 9: Designing Schemas that Scale: Arrays and Structs in BigQuery
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 Lab: Schema Design for Performance: Arrays and Structs in BigQuery
Module 10: Optimizing Queries for Performance
Walkthrough of a BigQuery Job Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs Optimize Queries for Cost
Module 11: Controlling Access with Data Security Best Practices
Data Security Best Practices Controlling Access with Authorized Views
Module 12: Predicting Visitor Return Purchases with BigQuery ML
Intro to ML Feature Selection Model Types Machine Learning in BigQuery Lab: Predict Visitor Purchases with a Classification Model with BigQuery ML
Module 13: Deriving Insights from Unstructured Data using Machine Learning
Structured vs Unstructured ML Prebuilt ML models Lab: Extract, Analyze, and Translate Text from Images with the Cloud ML APIs Lab: Training with Pre-built ML Models using Cloud Vision API and AutoML
Module 14: Completion
Summary and course wrap-up