Python and Pandas for Data Engineering - Pragmatic AI
This course is part of Data Engineering Foundations.
This comprehensive course teaches the fundamentals of Python and Pandas for data engineering applications. Students learn to set up version-controlled Python environments, write effective programs using key language features, and manipulate data using Pandas. The curriculum covers essential concepts including package management, data structures, DataFrame operations, and alternatives like NumPy arrays and PySpark. Through hands-on exercises, participants gain practical experience with modern development tools like Vim, Visual Studio Code, and Git. The course provides a solid foundation for both beginners and those with some programming experience.
4.6
(27 ratings)
Instructors:
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What you'll learn
Set up and manage Python development environments with version control
Master core Python syntax and essential data structures
Manipulate and analyze data using Pandas DataFrames
Work with alternative data structures like NumPy arrays and PySpark
Develop efficiently using Vim, Visual Studio Code, and Git
Skills you'll gain
This course includes:
PreRecorded video
Quizzes, Labs, Final Challenge
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 4 modules in this course
The course provides comprehensive training in Python programming and data manipulation using Pandas. Students learn environment setup, core Python syntax, data structures, and DataFrame operations. The curriculum includes hands-on practice with development tools like Vim and Git, preparing learners for real-world data engineering tasks.
Getting Started with Python
Module 1
Essential Python
Module 2
Data in Python: Pandas and Alternatives
Module 3
Python Development Environments
Module 4
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: Data Engineering Foundations
Instructors
Executive in Residence and Founder of Pragmatic AI Labs at Duke University
Noah Gift is the founder of Pragmatic AI Labs and serves as an Executive in Residence at Duke University, where he lectures in the Master of Interdisciplinary Data Science (MIDS) program. He specializes in designing and teaching graduate-level courses on machine learning, MLOps, artificial intelligence, and data science, while also consulting on machine learning and cloud architecture for students and faculty. A recognized expert in the field, Gift is a Python Software Foundation Fellow and an AWS Machine Learning Hero, holding multiple AWS certifications, including AWS Certified Solutions Architect and AWS Certified Machine Learning Specialist. He has authored several influential books, such as Practical MLOps, Python for DevOps, and Pragmatic AI, and has published over 100 technical articles across various platforms, including Forbes and O'Reilly. His extensive industry experience includes roles as CTO and Chief Data Scientist for notable companies like Disney Feature Animation, Sony Imageworks, and AT&T, contributing to major films like Avatar and Spider-Man 3. Gift's work has generated millions in revenue through product development on a global scale. He actively consults startups on machine learning and cloud architecture while leading initiatives to enhance data science education.
Senior Data Engineer and Educator at Duke University
Kennedy Behrman is a Senior Data Engineer at Duke University, where he also serves as an instructor for several online courses focused on data engineering and visualization. With decades of experience in Python and data management across various fields, including film, computing, and machine learning, he has established himself as a leading figure in the industry. Behrman has developed and taught courses such as "Data Visualization with Python" and "Linux and Bash for Data Engineering," equipping students with essential skills for the evolving data landscape. His expertise extends to big data processing technologies, where he covers platforms like Apache Spark and Snowflake. In addition to his teaching roles, Behrman has authored educational materials that contribute to the understanding of data science principles. His commitment to fostering learning and innovation in data engineering makes him a valuable asset to both Duke University and the broader academic community.
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