- Scope: 1,5h interactive session on Loops and Conditions
- Level: All
- Format: Online
This training is available on demand. Please contact Rosina Preis via rosina.preis@eitmanufacturing.eu for further information.
€ 150,00
In stock
excl. 20% VAT
This training is available on demand. Please contact Rosina Preis via rosina.preis@eitmanufacturing.eu for further information.
In stock
Dive into the dynamic world of Python programming with our online training “Decoding Python Logic: An Interactive Session on Loops and Conditions”. Join us for an engaging session where we unravel the intricacies of Python’s looping structures and conditions, empowering you to optimize your code for maximum efficiency. Through real-time examples, hands-on exercises, and expert insights, this webinar is designed to elevate your coding prowess, ensuring you leave with a comprehensive understanding of how to implement and leverage loops and conditions in Python, one of the most important main building blocks in the programming environment. This session promises an immersive experience that will transform your approach to Python programming.
This training is part of the learning path “Industrial Data Science” and may be combined with other sessions.
Main learning outcomes:
Who is this training for:
“Decoding Python Logic: An Interactive Session on Loops and Conditions” is a 1,5h online training which will include the following topics:
After the training, the course materials and training will be available on demand. For on-demand training and further information, please contact Rosina Preis via rosina.preis@eitmanufacturing.eu.
Traner 1: Dipl.-Ing. Linus Kohl
Linus Kohl works as a research associate at TU Wien and Project Manager at Fraunhofer Austria. He has experience in leading and managing research projects involving topics related to predictive and prescriptive maintenance, data analytics and developing novel machine learning solutions.
Trainer 2: Dipl.-Ing. Theresa Madreiter
Theresa Madreiter works as a research associate at TU Wien and Fraunhofer Austria. She has experience in leading and managing research projects. Her focus is on utilizing data for improved maintenance solutions, with a specialty in predictive maintenance and the analysis of unstructured data.