What you'll learn:
Learn why so many companies are leveraging cloud technology for data science & analytics
Understand the basic use cases of cloud computing for different data roles (analysts, data scientists & data engineers)
Compare the different types of cloud services (IaaS, PaaS, and SaaS) and cloud infrastructure (private, public & hybrid)
Explore the similarities and differences between major players in the cloud landscape, including Amazon Web Services, Microsoft Azure & Google Cloud
Demo real-world data analytics workflows using Azure, GCP, AWS and Snowflake
Requirements:
This is an entry-level course (no prerequisites)
Some experience working with data is helpful, but not required
Description:
This course is a high-level introduction to the world of cloud computing.The cloud ecosystem has grown exponentially in recent years, now storing more than half of the world's corporate data. Yet most people who interact with cloud services are unaware of what's going on behind the scenes.In this course, we'll set the stage by defining what cloud computing means and why companies rely on it, draw comparisons against traditional on-premise computing, and explore how different types of data professionals interact with cloud technology.From there we'll dig into the core components of cloud architecture, compare different types of cloud services and infrastructure, and review important topics like security, virtualization, cost control, and more.Next, we'll explore the modern cloud landscape, and compare services offered by key players like AWS, Microsoft Azure, and Google Cloud. We'll introduce public and private cloud providers, data platforms and software products, and discuss how to mitigate the risk of vendor lock-in.Last but not least, we'll walk through unique demos and real-world use cases to showcase how you can begin to leverage these services as a data professional, including workflows built on AWS, Azure, GCP and Snowflake.COURSE OUTLINE:Cloud 101Introduce the basics of cloud computing, including what it is, why companies use it, and the way different data roles interact with itCloud ArchitectureUnderstand the core components of cloud computing and cloud infrastructure, as well as the types of cloud services and architectureThe Cloud LandscapeReview some of the major players in the cloud computing industry for data analytics, and compare their similarities and differencesCloud Data StacksDemo simple data analytics pipelines using combinations of cloud products and services (or "stacks"), including AWS, MySQL Workbench, GCP, Looker, Azure, Snowflake, and more__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:2 hours of high-quality video4 real-world cloud demos & case studies3 course quizzesCloud Basics for Data Professionals ebook (50+ pages)Expert support and Q&A forum30-day Udemy satisfaction guaranteeWhether you're an analyst or data scientist interested in cloud computing or a business leader looking to learn about the cloud landscape, this course is for you.Happy learning!-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)
Who this course is for:
Everyday people looking to build a basic understanding of cloud technology for data analytics
Leaders looking to learn more about the cloud landscape and ecosystem
Data professionals who want an introduction to modern cloud data stacks and workflows
Anyone looking to get familiar with major cloud players like AWS, Azure, GCP and Snowflake

https://rapidgator.net/file/f5d88e47a408445bf613c6a6becb9328/Cloud_Basics_for_Data_Professionals.rar.html




What you'll learn:
Learn why so many companies are leveraging cloud technology for data science & analytics
Understand the basic use cases of cloud computing for different data roles (analysts, data scientists & data engineers)
Compare the different types of cloud services (IaaS, PaaS, and SaaS) and cloud infrastructure (private, public & hybrid)
Explore the similarities and differences between major players in the cloud landscape, including Amazon Web Services, Microsoft Azure & Google Cloud
Demo real-world data analytics workflows using Azure, GCP, AWS and Snowflake
Requirements:
This is an entry-level course (no prerequisites)
Some experience working with data is helpful, but not required
Description:
This course is a high-level introduction to the world of cloud computing.The cloud ecosystem has grown exponentially in recent years, now storing more than half of the world's corporate data. Yet most people who interact with cloud services are unaware of what's going on behind the scenes.In this course, we'll set the stage by defining what cloud computing means and why companies rely on it, draw comparisons against traditional on-premise computing, and explore how different types of data professionals interact with cloud technology.From there we'll dig into the core components of cloud architecture, compare different types of cloud services and infrastructure, and review important topics like security, virtualization, cost control, and more.Next, we'll explore the modern cloud landscape, and compare services offered by key players like AWS, Microsoft Azure, and Google Cloud. We'll introduce public and private cloud providers, data platforms and software products, and discuss how to mitigate the risk of vendor lock-in.Last but not least, we'll walk through unique demos and real-world use cases to showcase how you can begin to leverage these services as a data professional, including workflows built on AWS, Azure, GCP and Snowflake.COURSE OUTLINE:Cloud 101Introduce the basics of cloud computing, including what it is, why companies use it, and the way different data roles interact with itCloud ArchitectureUnderstand the core components of cloud computing and cloud infrastructure, as well as the types of cloud services and architectureThe Cloud LandscapeReview some of the major players in the cloud computing industry for data analytics, and compare their similarities and differencesCloud Data StacksDemo simple data analytics pipelines using combinations of cloud products and services (or "stacks"), including AWS, MySQL Workbench, GCP, Looker, Azure, Snowflake, and more__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:2 hours of high-quality video4 real-world cloud demos & case studies3 course quizzesCloud Basics for Data Professionals ebook (50+ pages)Expert support and Q&A forum30-day Udemy satisfaction guaranteeWhether you're an analyst or data scientist interested in cloud computing or a business leader looking to learn about the cloud landscape, this course is for you.Happy learning!-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)
Who this course is for:
Everyday people looking to build a basic understanding of cloud technology for data analytics
Leaders looking to learn more about the cloud landscape and ecosystem
Data professionals who want an introduction to modern cloud data stacks and workflows
Anyone looking to get familiar with major cloud players like AWS, Azure, GCP and Snowflake

https://rapidgator.net/file/f5d88e47a408445bf613c6a6becb9328/Cloud_Basics_for_Data_Professionals.rar.html