Analytics Engineering for Data Professionals
10 Days
·Cohort-based Course
🔥 Code OFF250 for $250 discount (4 redemptions left)🔥 The course: spin up a modern Data Stack using Snowflake, Fivetran, dbt / SQL, Preset
Analytics Engineering for Data Professionals
10 Days
·Cohort-based Course
🔥 Code OFF250 for $250 discount (4 redemptions left)🔥 The course: spin up a modern Data Stack using Snowflake, Fivetran, dbt / SQL, Preset
Previously at
Course overview
Analytics Engineering is the foundation of Data Science and AI
Analytics Engineering is a dynamic blend of Data Engineering and Data Analytics, serving as the bridge between these two domains. Analytics Engineers develop and maintain a good portion of the data lifecycle, from the ingestion of Data Sources, to the development of Data Warehouses and the corresponding data pipelines, to Business Intelligence tools. Whether you are a Data Analyst/Scientist that would like to practice on modern Data Engineering tools, or an aspiring Analytics Engineer, this workshop is for you.
You will learn how to:
● Start and develop a modern Data Warehouse, using Snowflake.
● Ingest data, on a schedule, from multiple sources, using connectors in Fivetran.
● Clean and transform data, and the basics of ELT (Extract, Load, Transform), using DBT and SQL.
● Spin up and connect a Business Intelligence tool (Preset) to a Data Warehouse.
Who is this course for
01
A Data Analyst or Data Scientist looking to sharpen their Data Engineering skills using modern data tools.
02
A recent graduate looking to land their first Analytics Engineering role with practical skills that are highly marketable.
03
A professional trying to pivot into a Data role and curious about what Analytics Engineering is about.
Key outcomes
Build an end-to-end data engineering product, from raw data to data visualization.
Start and develop a modern Data Warehouse, using Snowflake.
Ingest data, on a schedule, from multiple sources, using connectors in Fivetran.
Clean and transform data using DBT and the universal data language of SQL.
Spin up and connect a Business Intelligence tool (Preset) to a Data Warehouse.
Add this hands-on project to your portfolio, showcasing new marketable skills.
This course includes
5 interactive live sessions
Lifetime access to course materials
6 in-depth lessons
Direct access to instructor
Projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Analytics Engineering for Data Professionals
Course syllabus
Week 1
Jan
13
Live session 1: Introduction + overview of Data Stack
Mon 1/137:00 PM—8:30 PM (UTC)Jan
14
Live Session 2: Data Warehouse in Snowflake and data ingestion using Fivetran
Tue 1/147:00 PM—8:30 PM (UTC)Jan
16
Live Session 3: Setting up Github and DBT
Thu 1/167:00 PM—8:30 PM (UTC)
Introduction to Analytics Engineering
Ingesting data into a Data Warehouse
Configuring data pipelines in DBT
Week 2
Jan
20
Live Session 4: SQL pipelines in DBT
Mon 1/207:00 PM—8:30 PM (UTC)Jan
22
Live Session 5: creating dashboards in Preset
Wed 1/227:00 PM—8:30 PM (UTC)
Cleaning and transforming data using DBT and SQL
Visualizing data with a Business Intelligence tool
Bonus
Improve your SQL skills (optional)
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Analytics Engineering for Data Professionals
Meet your instructors
Fabrizio
Fabrizio built and scaled both leadership and technical data teams at Uber Eats and Better Mortgage.
As an academic, he published 20+ peer-reviewed articles in top Statistics, Machine Learning, and Medicine journals.
He also teaches graduate courses at Columbia University (in the past: Carnegie Mellon University and Duke University), where he develops the next generations of data leaders.
Mattia
Mattia worked as a technical leader at Amazon Alexa and PayPal, where he contributed to the design and implementation of both experimental and production-grade Machine Learning and Artificial Intelligence systems.
Mattia is also a Teaching Professor at Carnegie Mellon University. Prior to Carnegie Mellon University, he was a Consulting Professor at Duke University. Every year, Mattia helps hundreds of students learn how to solve real-world problems using Data Science and Machine Learning.
Course schedule
4-6 hours per week
Week 1 - Mon, Tues, Thu
2:00pm - 3:30pm EST
- Monday: introduction to the Modern Data Stack
- Tuesday: Snowflake and Fivetran
- Thursday: dbt and Github
Live sessions will be recorded.
Week 2 - Mon, Wed
2:00pm - 3:30pm EST
- Monday: transforming data with DBT and SQL
- Wednesday: dashboarding in Preset
Live sessions will be recorded.
Course project
2 hours per week
Follow the course by implementing your own Data Stack!
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Analytics Engineering for Data Professionals
Learning is better with cohorts
Active learning, not passive watching
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Frequently Asked Questions
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