Coursera – DeepLearning.AI Data Engineering Professional Certificate 2024-9

DeepLearning.AI Data Engineering Professional Certificate course. This course teaches you the fundamentals of effective data engineering. By learning the skills needed in the increasingly popular field of data engineering, you’ll learn how to create real business value using a core set of principles and strategies for developing data systems. The focus of this program is to provide basic training in data engineering that includes conceptual knowledge and principles, along with practical skills for building data systems on the AWS cloud. You will learn about the phases of the data engineering life cycle including generating, ingesting, storing, transforming and presenting data, as well as life cycle infrastructure such as security, DataOps, data management, data architecture, orchestration and software engineering. You’ll also learn how to design a good data architecture, choose tools and technologies, and build data pipelines on the AWS cloud.

What you will learn

• Comprehensive Mental Model of Data Engineering: Gain a thorough understanding of the data engineering lifecycle and its infrastructure.
• A framework for approaching any data engineering project: learning an effective way to create business value with data.
• Proficiency in the five stages of the data engineering life cycle: generating, receiving, storing, transforming and presenting data.
• Principles of good data architecture: Applying these principles to building data systems on the AWS cloud.
• Prepare for a career in IT: Get professional training from DeepLearning.AI.
• Showcase skills in resume-ready projects: Create projects that showcase your abilities.
• Get a valid certificate from DeepLearning.AI: Get a certificate recognized by employers.

Who is this course suitable for?

• Prospective or current data engineers: Interested in learning the principles and practical skills of data engineering.
• Data professionals in related roles: such as software engineering, data science or analytics who are looking to expand and deepen their skills in data engineering.
• Other data stakeholders: such as data team leaders and technical business leaders seeking to understand enterprise-wide data systems and structures.