Data Engineering Fundamentals for AI
Learn essential data engineering skills for AI applications, including data management, SQL, Python, and visualization techniques.
Learn essential data engineering skills for AI applications, including data management, SQL, Python, and visualization techniques.
Master the foundations of data engineering for AI applications in this comprehensive course. Learn why data management is crucial for AI success and how to properly handle data throughout the machine learning lifecycle. Develop practical skills in SQL querying, Python notebooks, pandas data manipulation, and data visualization using Seaborn. The course covers both theoretical concepts and hands-on implementation, preparing you for real-world AI development.
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What you'll learn
Understand the importance of data management in AI applications
Master data extraction and querying using SQL
Set up and configure Python notebook environments
Develop proficiency in pandas for data manipulation
Create effective data visualizations with Seaborn
Implement complete data pipelines for AI projects
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This course provides a comprehensive introduction to data engineering for AI applications. Students learn the fundamentals of data management, including data requirements for AI systems, database concepts, and data processing techniques. The curriculum covers practical skills in SQL querying, Python notebooks setup, pandas data manipulation, and visualization using Seaborn. Special emphasis is placed on understanding why proper data management is crucial for AI project success.
Data Management for AI
Module 1
Data Management Fundamentals
Module 2
SQL and Data Extraction
Module 3
Python Notebook Setup
Module 4
Advanced Pandas
Module 5
Data Visualization
Module 6
Fee Structure
Instructors
3 Courses
A Leading Authority in Data Engineering and Semantic Systems
Dr. Christoph Lofi serves as an Associate Professor in the Web Information Systems group at TU Delft's Faculty of Engineering, Mathematics and Computer Science, where he focuses on developing semantic-based data engineering methodologies for FAIR (Findable, Accessible, Interoperable, Reusable) data management platforms. His research addresses critical challenges in extracting knowledge from unstructured data, handling meta-data, and semantic query processing, with particular emphasis on high-impact societal domains including agricultural sciences, public health, and nutrition. As Director of Studies for BSc Computer Science and Engineering, he has made significant contributions to data education, earning recognition through three nominations for the Teacher of The Year Awards. His academic impact is evidenced by over 1,500 citations and an h-index of 24, reflecting his substantial contributions to machine learning bias mitigation, data engineering, and semantic systems. Currently, he leads several data management courses at TU Delft while actively working to strengthen data education both within and outside the university, particularly through developing AI Skills courses focusing on data engineering and pipeline development.
3 Courses
A Pioneer in Open Aviation Science and Sustainable Air Transportation
Dr. Junzi Sun serves as an Assistant Professor at TU Delft's Faculty of Aerospace Engineering, where he leads innovative research in air traffic management and sustainable aviation. His academic journey began with telecommunications and computer science in China, followed by aerospace studies in Europe, culminating in his PhD from TU Delft focusing on aircraft performance modeling. His significant contributions include developing widely-used open-source tools like pyModeS and OpenAP, and authoring the comprehensive open-access book "The 1090 Megahertz Riddle." As Editor-in-Chief of the Journal of Open Aviation Science, he champions open science principles in aviation research. His research portfolio spans aircraft surveillance technologies, trajectory optimization, and environmental impact assessment of aviation, with his work garnering over 1,400 citations. His OpenAP aircraft performance model has become a standard tool in air transportation studies, while his work on flight data analysis and sustainability has earned him the SESAR Young Scientist Award in 2019. Currently, he leads projects investigating contrail formation mitigation and developing AI-based solutions for trajectory prediction, while continuing to advocate for open data and reproducible research in aviation science.
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