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Analytics Engineering for Beginners
Bestseller
Highest Rated
Rating: 4.7 out of 5(34 ratings)
278 students

Analytics Engineering for Beginners

Learn what analytics engineers do, how modern data teams work, and the tools and concepts you need to break in
Created byMaven Analytics (2,000,000+ Learners), Alice Zhao
Last updated 6/2026
English

What you'll learn

  • 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

Course content

9 sections • 43 lectures • 1h 25m total length

  • Course Introduction1:43

    Explore the analytics engineering role in modern data teams, data architectures, pipelines, and the modern data stack, with terms like databases, data warehouses, data lakes, OLTP, OLAP, ETL, ELT.

  • Meet the Instructor0:21

    Meet the instructor, Alice Zhao, an author of SQL Pocket Guide and Maven Analytics staff instructor, who introduces data science, analytics engineering, Python, and SQL concepts for beginners.

  • Course Outline1:35

    Explore the analytics engineering field, differentiate analytics engineers from data engineers and data analysts, and learn key terms, the modern data stack, OLTP vs OLAP, and ETL vs ELT.

  • READ ME: Important Notes for New Students2:18
  • DOWNLOAD: Course Resources0:08
  • Setting Expectations1:29

Requirements

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

Description

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.

Who this course is for:

  • Aspiring analytics engineers looking to transition or break into the field
  • Data professionals who want a better understanding of the analytics engineering role
  • Anyone who wants to learn how modern data teams move, transform, and organize data

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