Applied Python Data Engineering (Duke University Coursera Specialization)

2026-04-17 · 4min · Pragmatic AI Labs

Table of Contents

Three courses covering applied Python data engineering — the big-data stack (Spark, Hadoop, Snowflake), the platform layer (Docker, Kubernetes, virtualization), and production-grade data visualization. Duke University's pragmatic bridge from Python scripting to data platforms.

Do you want to learn Scripting with Python and SQL for Data Engineering?

Learn essential data engineering skills through practical Python scripting and SQL database management. Master web scraping, data processing, and database operations while building real-world data engineering solutions.

Check out our course!

Enroll on Coursera →

What You Will Build

Spark jobs that scale across clusters, Snowflake-backed analytics pipelines, containerized data workloads on Kubernetes, and publication-quality Python visualization dashboards.

Courses in This Specialization

Spark, Hadoop, and Snowflake for Data Engineering — The distributed-computing and warehousing stack every data engineer needs.
Virtualization, Docker, and Kubernetes for Data Engineering — Containers and orchestration for reproducible, scalable pipelines.
Data Visualization with Python — Matplotlib, Plotly, and Python-native dashboarding.

Who This Is For

Python engineers moving into data engineering
Data analysts scaling beyond single-machine pandas
Platform engineers productionizing data workloads

Python, Bash and SQL Essentials for Data Engineering — the scripting foundations this builds on
Building Cloud Computing Solutions at Scale — cloud deployment context
Enterprise AI and Data Engineering with Databricks — lakehouse-native data engineering

Want expert ML and AI training?

From the fastest growing platform in the world.

Start for Free

Recommended Courses

Based on this article's content, here are some courses that might interest you:

Scripting with Python and SQL for Data Engineering (4 weeks)
Learn essential data engineering skills through practical Python scripting and SQL database management. Master web scraping, data processing, and database operations while building real-world data engineering solutions.
Python Essentials for MLOps (5 weeks)
Learn essential Python programming skills required for modern Machine Learning Operations (MLOps). Master fundamentals through advanced concepts with hands-on practice in data science libraries and ML application development.
DevOps, DataOps, and MLOps (5 weeks)
Learn to build and deploy production-ready machine learning systems using modern DevOps and MLOps practices. Master essential tools and frameworks while implementing end-to-end ML pipelines.
DevOps, DataOps, and MLOps (5 weeks)
Learn to build and deploy production-ready machine learning systems using modern DevOps and MLOps practices. Master essential tools and frameworks while implementing end-to-end ML pipelines.
Data Engineering with Databricks (2 weeks)
Learn professional data engineering using the Databricks platform and its comprehensive suite of tools. Master essential skills in data transformation, pipeline management, and enterprise-grade data architecture while working with real-world scenarios.