FREE Course
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.
Course Content
2 course hours
6 quizzes
Who should take this course
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
Meet your instructors
Alice Zhao
Lead Data Science Instructor
Alice Zhao is a seasoned data scientist and author of the book, SQL Pocket Guide, 4th Edition (O'Reilly). She's an adjunct lecturer for Northwestern University's Machine Learning and Data Science program, where she teaches Python, SQL, R, data warehousing and data visualization.
Featured review
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Analytics Engineering 101
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
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.
Curriculum
Meet your instructors
Alice Zhao
Lead Data Science Instructor
Alice Zhao is a seasoned data scientist and author of the book, SQL Pocket Guide, 4th Edition (O'Reilly). She's an adjunct lecturer for Northwestern University's Machine Learning and Data Science program, where she teaches Python, SQL, R, data warehousing and data visualization.
Student reviews
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Analytics Engineering 101
Analytics Engineering 101
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
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