Duration 3 Days 18 CPD hours This course is intended for 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 Overview 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 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. 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 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 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 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 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 Storing and Exporting Data Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Creating new Permanent Tables 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 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 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 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 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 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 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 Advanced Insights Introducing Cloud Datalab Cloud Datalab Notebooks and Cells Benefits of Cloud Datalab Data Access Compare IAM and BigQuery Dataset Roles Avoid Access Pitfalls Review Members, Roles, Organizations, Account Administration, and Service Accounts
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Just enrol & start learning. Career path Completing our Full Stack Web Development Training program opens up exciting career opportunities in the UK tech industry. Become a sought-after web developer, secure positions in top companies, work as a freelance developer, or even start your own web development agency. The possibilities are endless! Certificates CPD Accredited e-Certificate Digital certificate - Included CPD Accredited Framed (Hardcopy) Certificate Hard copy certificate - Included Enrolment Letter Digital certificate - Included Student ID Card Digital certificate - Included The Quality Licence Scheme Endorsed Certificate of Achievement Digital certificate - Included
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Course Curriculum Section 01: Introduction Introduction 00:02:00 Section 02: IT Fundamentals Server 00:08:00 What is hardware and software? 00:04:00 Project Life Cycle 00:06:00 Software Development Life Cycle 00:08:00 Section 03: IT Terms / basics Methodologies: Scrum, Agile, Kanban, Waterfall, Lean 00:09:00 CMS 00:04:00 API 00:04:00 Stacks 00:03:00 Native & Hybrid Native Apps 00:02:00 CI / CD / Deployment tools 00:04:00 Section 04: IT Positions CTO 00:13:00 IT Architects 00:08:00 Product Owner 00:12:00 Product Manager 00:06:00 PO vs Product Manager 00:04:00 Project Manager 00:13:00 Product Owner vs Project Manager vs Product Manager 00:07:00 Business Analysts 00:09:00 Business Intelligence Analyst 00:06:00 Data Engineer 00:05:00 Data Scientist 00:07:00 Data Engineer vs Data Scientist 00:07:00 Agile Coach 00:14:00 Scrum Master 00:08:00 Agile Coach vs Scrum Master 00:01:00 Frontend Developer 00:06:00 Backend Developer 00:06:00 Fullstack Developer 00:04:00 Developers: Frontend, Backend and Fullstack 00:02:00 iOS Developer 00:03:00 Android Developer 00:04:00 UX Designers 00:09:00 UI Design 00:08:00 UX vs UI Design: what is the difference? 00:04:00 QA Engineers and Testers 00:09:00 System Administrators+ 00:07:00 DevOps 00:05:00 SEO Specialists: who are they? 00:10:00 Section 05: IT Technologies Programming languages and frameworks Programming Languages and Frameworks. Introduction 00:01:00 Programming languages and frameworks 00:08:00 JavaScript 00:02:00 Java 00:03:00 Python 00:01:00 C 00:02:00 C# 00:02:00 C++ 00:03:00 SWIFT 00:02:00 Objective C 00:01:00 HTML 00:02:00 PHP 00:02:00 Ruby 00:02:00 SQL 00:02:00 Go 00:01:00 R 00:01:00 Section 06: IT Technologies: Databases Database 00:05:00 Types of Databases 00:07:00 Section 07: From Recruiter to Recruiter The day of an IT Recruiter 00:05:00 Key principles 00:03:00 Sourcing Tipps 00:03:00 Section 08: Bye-bye Thank you for taking part in this course! 00:01:00 Assignment Assignment - Recruitment: IT Basics for IT Recruiters 00:00:00