This course is part of Python, Bash and SQL Essentials for Data Engineering.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Python, Bash and SQL Essentials for Data Engineering Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
What you'll learn
Construct Python Microservices with FastAPI
Build powerful command-line tools using Click
Master cloud-hosted Jupyter notebooks
Implement continuous integration workflows
Deploy containerized machine learning services
Skills you'll gain
This course includes:
4.2 Hours PreRecorded video
10 quizzes, 7 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on building web applications and command-line tools essential for data engineering. Students learn to construct Python microservices with FastAPI, develop command-line tools using Click, and work with cloud-hosted Jupyter notebooks. The curriculum covers project structure, continuous integration, package distribution, and containerization. Through hands-on projects, learners gain practical experience in creating scalable data engineering solutions and automated testing workflows.
Jupyter Notebooks
Module 1 · 4 Hours to complete
Cloud-Hosted Notebooks
Module 2 · 5 Hours to complete
Python Microservices
Module 3 · 12 Hours to complete
Python Packaging and Rust Command Line Tools
Module 4 · 19 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Python, Bash and SQL Essentials for Data Engineering
Instructors
Adjunct Assistant Professor at Duke University
Dr. Alfredo Deza is an Adjunct Assistant Professor in the Pratt School of Engineering at Duke University, where he teaches courses on machine learning, programming, and data engineering. He has been involved in academia for several years, focusing on innovative teaching methods and practical applications of technology. Dr. Deza co-authored the book Practical MLOps and has published several other works related to Python and machine learning. His teaching includes courses such as Python Bootcamp and advanced data engineering topics, and he actively develops online courses available on platforms like Coursera. In addition to his academic role, Dr. Deza works in developer relations at Microsoft, leveraging his extensive experience in software engineering and cloud computing to enhance educational content and support for students and faculty. He collaborates with various universities worldwide, including Georgia Tech and Carnegie Mellon University, to promote knowledge sharing in the field of technology and data science.
Executive in Residence and Founder of Pragmatic AI Labs
Noah Gift is the founder of Pragmatic AI Labs and serves as an Executive in Residence at Duke University, where he lectures in the MIDS Graduate Data Science Program. With nearly two decades of experience, he specializes in machine learning, MLOps, and cloud architecture, designing graduate-level courses that bridge theory and practical applications. Gift is a recognized expert in the field, being a Python Software Foundation Fellow and an AWS Machine Learning Hero, with extensive experience working with major companies such as Disney Feature Animation, Sony Imageworks, and AT&T. He has authored several influential books, including Practical MLOps and Python for DevOps, and has published numerous technical articles across various platforms.In his teaching role, Gift offers a wide range of courses such as Advanced Data Engineering, Cloud Machine Learning Engineering and MLOps, and Data Visualization with Python. His work emphasizes hands-on learning and real-world problem-solving using contemporary tools in AI and cloud computing. By leveraging his industry experience and academic insights, Gift aims to equip students with the skills necessary to tackle complex challenges in data science and machine learning, preparing them for successful careers in these rapidly evolving fields.
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