Dear friends,
Years ago, when I was working at a large tech company, I was responsible for the data warehouse. Every piece of data relating to individual users was supposed to come through the data warehouse, and it was an intellectually challenging undertaking to store the data reliably and make it available to other teams, subject to security and privacy guardrails, so they could use it to derive insights.
I wish that, back then, I (and my whole team) had had access to the Data Engineering Professional Certificate, a major new specialization we just launched on Coursera!
Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. Today, far too many businesses struggle to build a robust data infrastructure, which leads to missed opportunities to create value with data analytics and AI. Additionally, AI’s rise is accelerating the demand for data engineers.
If you’re interested in learning these skills, please check out this four-course sequence, which is designed to make you job-ready as a data engineer.
The Data Engineering Professional Certificate is taught by Joe Reis, co-author of the best-selling book Fundamentals of Data Engineering, in collaboration with Amazon Web Services. (Disclosure: I serve on Amazon's board of directors.) When DeepLearning.AI decided to teach data engineering, I felt that Joe, who has helped many startups and big companies design their data architectures and thus has broad and deep experience in this field, would be the ideal instructor. He was the first person we reached out to, and I was thrilled that he agreed to work with us on this. I hope that you’ll be thrilled, too, taking this specialization!
While building AI systems and analyzing data are important skills, the data that we feed into these systems determines their performance. In this specialization, you’ll go through the whole data engineering lifecycle and learn how to generate, ingest, store, transform, and serve data. You’ll learn how to make necessary tradeoffs between speed, flexibility, security, scalability, and cost.
If you’re a software engineer, this will give you a deeper understanding of data engineering so that you can build data applications. If you’re an aspiring or practicing data scientist or AI/machine learning engineer, you’ll learn skills that expand your scope to manage data in a more sophisticated way. For example, you’ll learn about DataOps to automate and monitor your data pipelines, and how to build “infrastructure as code” to programmatically define, deploy, and maintain your data infrastructure, as well as best practices for data-centric AI.
You’ll also hear 17 other industry leaders share their wisdom about effective data engineering. Bill Inmon, the father of data warehousing, shares fascinating stories about the evolution of the data warehouse, including how he wrote his first program as a student in 1965. Wes McKinney, creator of the Python pandas package (as in “import pandas as pd”), talks about how he designed this wildly popular package and shares best practices for data manipulation. These instructors will give you a mental framework for developing and deploying data systems.
Getting your data infrastructure right is a valuable foundational skill that will serve you well in whatever you do with AI or data analytics. I hope you enjoy this specialization!
Keep learning,
Andrew