Analytics Engineering for Beginners

Descripción

This course is designed to give you a clear, practical understanding of what analytics engineering is, why it exists, and what you need to know to get started. We'll kick things off by defining the analytics engineering role: its history, how it fits into a modern data team, and why it's become essential as companies deal with more data, more tools, and more complexity than ever before. From there, we'll get into the technical foundations. You'll learn about data architectures, pipelines, and the modern data stack — the collection of tools and technologies that analytics engineers work with every day. To make things concrete, we'll walk through three real-world scenarios where an analytics engineer adds value. These are the kinds of problems you'll actually encounter on the job, and they'll help you see how the role shows up in practice. Next, we'll shift gears and dig into the key data terms and acronyms that every analytics engineer needs to know — from the difference between databases, data warehouses and data lakes, to OLTP vs. OLAP and ETL vs. ELT. We'll break down what each one means, why it matters, and how they describe data movement between systems in the real world. Whether you're exploring analytics engineering as a career path, transitioning from an analyst or engineering role, or just trying to understand how modern data teams operate, this is the course for you.

Lo que aprenderás

• Define the analytics engineering role and explain how it fits into a modern data team
• Describe the components of the modern data stack, including data architectures and pipelines
• Distinguish between key concepts like OLTP vs. OLAP, ETL vs. ELT, and databases vs. data warehouses vs. data lakes
• Identify common real-world scenarios where an analytics engineer adds value

Requisitos

• Basic understanding of data and databases is helpful, but not required (this is a beginner-friendly course)