This course introduces key computer science concepts and data science techniques tailored to generative AI coding tools, equipping students with the skills to effectively use these tools for programming, data analysis, and visualization in computational economics and data science. While prior experience in programming or data science is helpful, it is not required. The course uniquely combines programming fundamentals with advanced topics like data management and visualization, illustrating workflows using state-of-the-art generative AI tools. Programming in R, exploratory data analysis with libraries like dplyr and TensorFlow, and visualization with ggplot2 are central components of the curriculum.
- Understand how and when to employ generative AI tools for implementing, debugging, and communicating coding content
- Understand and apply programming concepts and practices to solve data science problems
- Utilize standard aggregation and visualization techniques for economic data
- Communicate technical data science ideas clearly and concisely
- Gain a high-level overview of the open-source R statistical software ecosystem and enrich and expand their knowledge of R libraries
This course is part of the prestigious part-time Goethe MBA Digital Transformation | Data Science | Sustainability, conducted in English on Fridays and Saturdays on Campus Westend. It offers a valuable opportunity to network and gain expertise without committing to a full degree program. Upon completion, participants receive a Certificate of Participation. This course can also be upgraded to a Certificate of Advanced Studies (CAS) in Data Science & AI in combination with other courses from our Goethe MBA. As the number of seats is limited, we recommend to register early. If you're a GBS or Goethe University alum, explore our attractive alumni discount options.
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Prof. Dr. Pantelis Karapanagiotis
Dr. Pantelis Karapanagiotis is a postdoctoral researcher in the Department of Operations at the University of Groningen. His expertise lies in mathematical microeconomics, focusing on efficient software solutions for large-scale mathematical decision modeling and econometric estimations using big data, statistical learning, and distributed technologies. His research interests include market models, game theory, and industrial organization. Dr. Karapanagiotis holds a background in mathematics and economics and has professional experience in software design and development. He is also a research affiliate at the Leibniz Institute for Financial Research SAFE and a researcher with the EurHisFirm consortium.