This is a self-paced lab that takes place in the Google Cloud console. In this lab you will learn how to use Dataprep in conjunction with AutoML Tables to build and operate your machine learning pipelines.

This course discusses common foundational principles and practices used by agile methodologies, providing you with a flexible set of tools to use in your role (e.g. product owner, scrum master, project manager, team member) on an agile team. Learn agile and lean principles, including kanban and scrum, and use Jira Software Cloud as the tool to apply hands-on exercises in these topics. The course includes instruction on company-managed and team-managed Jira projects. After completing this course, you will have a strong foundational understanding of agile principles and practices and hands-on experience with Jira Software Cloud. Site administrators will be able to perform basic administration tasks. You will be able to continuously configure your Jira projects to match your team's custom agile methodology. Once completing this course, you will be prepared to earn your first Atlassian credential: https://go.atlassian.com/coursera-apb-330 *You can use a free Jira Software Cloud site to complete the hands-on labs associated with this course. This will require a Windows, Mac, or Linux computer.

Building on “Data Warehousing and Business Intelligence,” this course focuses on data visualization and visual analytics. Starting with a thorough coverage of what data visualization is and what type of visualization is good for a given purpose, the course quickly dives into development of practical skills and knowledge about visual analytics by way of using one of the most popular visual analytics tools: SAS Viya, a cloud-based analytics platform. An overview of cloud architecture, automation, and machine learning is also provided.

This course focuses on advanced, yet essential concepts, techniques, and algorithms needed for understanding and designing modern wireless communication systems. You will begin this course by exploring the basics of wireless channels, followed by the need for multi-antenna systems. You will further learn about the two seminal multi-antenna technologies: MIMO and massive MIMO. The first is the basis for 4G systems, and the second is the same for 5G systems. You will also learn the multiple access techniques like orthogonal frequency division multiplexing (OFDM) and non-orthogonal multiple access (NOMA) and the basics of mmWave communications .

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will create a Flutter app with a TabView to isolate data. Add navigation to the application to move directly to a page.

This course covers Network Function Virtualization from the perspective of a systems engineer. It first discusses the proliferation of middleboxes in enterprise networks and the necessity of virtualizing these components just like software applications. It then details several optimizations in operating systems to facilitate efficient networking on virtualized hardware followed by design decisions in user-plane libraries for bypassing the kernel for efficient packet processing. It then covers the challenges and solutions for building scalable systems for providing networking services in datacenters, specifically the synergy between SDN and NFV to achieve this. The course concludes with a discussion of real-world applications of NFV technology in the cloud computing ecosystem.

Most introductory or beginner level cybersecurity courses are not truly beginner level. Most of them assume some level of technical competence and expect that cybersecurity is not your first technical job role. However, as I've successfully mentored people coming from fields such as nursing, aviation (an airline pilot!), and real estate, I've learned that these people are underserved as far a true introduction. This Learning Path is an answer to that gap.

Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab, you will learn how to backup an existing Microsoft SQL Database with Actifio

Welcome to the second course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn to design Cloud-native systems with the fundamental building blocks of Cloud computing. These building blocks include virtual machines and containers. You will also learn how to build effective Microservices using technologies like Flask and Kubernetes. Finally, you will analyze successful patterns in Operations including: Effective alerts, load testing and Kaizen. This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you build a containerized Flask application that is continuously deployed to a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP).