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Data Modeling for Analytics Engineering
Data Modeling for Analytics Engineering
Course Description
This course is designed to give you a solid foundation in data modeling, from core concepts to a step-by-step process that you can apply on the job.
If analytics engineering is about transforming raw data into clean, reliable datasets, then data modeling is the blueprint. It's how you decide what to build, how to structure it, and how to make sure it holds up as your data changes over time.
We'll start by introducing the key building blocks — data models, entities, and relationships — and draw a clear line between data modeling in general and dimensional modeling specifically.
From there, we'll break down the core unit of any data model: the table. You'll learn the main characteristics of tables in a database, then explore the two types you'll find in dimensional modeling — fact tables and dimension tables — and what makes each one unique.
Next, we'll zoom out and look at how those tables fit together. We'll introduce popular schema designs, like the star schema, and talk about how to arrange your facts and dimensions in a way that's clean and easy to query.
But a data model isn't static — data changes, and your model needs to handle that. We'll cover strategies for managing those changes, including slowly changing dimensions, so your models stay accurate without breaking downstream reports.
Last but not least, we'll put it all into practice. You'll see how data flows through a modern analytics workflow, and then walk through the full data modeling process — from scoping requirements to designing and transforming tables — just like an analytics engineer would on the job.
Whether you're building your first dimensional model or looking to formalize your approach, this course will give you the theory and process to do so with confidence.
Course Content
3 course hours
7 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 data modeling theory and steps
Anyone who wants to learn how modern data teams organize data for analysis
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
Data Modeling for Analytics Engineering
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 solid foundation in data modeling, from core concepts to a step-by-step process that you can apply on the job.
If analytics engineering is about transforming raw data into clean, reliable datasets, then data modeling is the blueprint. It's how you decide what to build, how to structure it, and how to make sure it holds up as your data changes over time.
We'll start by introducing the key building blocks — data models, entities, and relationships — and draw a clear line between data modeling in general and dimensional modeling specifically.
From there, we'll break down the core unit of any data model: the table. You'll learn the main characteristics of tables in a database, then explore the two types you'll find in dimensional modeling — fact tables and dimension tables — and what makes each one unique.
Next, we'll zoom out and look at how those tables fit together. We'll introduce popular schema designs, like the star schema, and talk about how to arrange your facts and dimensions in a way that's clean and easy to query.
But a data model isn't static — data changes, and your model needs to handle that. We'll cover strategies for managing those changes, including slowly changing dimensions, so your models stay accurate without breaking downstream reports.
Last but not least, we'll put it all into practice. You'll see how data flows through a modern analytics workflow, and then walk through the full data modeling process — from scoping requirements to designing and transforming tables — just like an analytics engineer would on the job.
Whether you're building your first dimensional model or looking to formalize your approach, this course will give you the theory and process to do so with confidence.
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
Data Modeling for Analytics Engineering
Data Modeling for Analytics Engineering
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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