What we’re about
Nürnberg Data Science is a new group for talented and experienced data scientists interested in sharing ideas, knowledge and expertise. As a group we can shape the next generation of intelligent algorithms and have an impact on our local ecosystem.
Upcoming events (1)
See all- Data Science & AI #21 - Crafting AI and Data Science Challengescodecentric Office Nürnberg, Nürnberg
For our final meetup of the year, we have an exciting program lined up:
- 🧠 A thought-provoking talk about the craft of AI and data science and the biggest challenges faced by AI projects in practice.
- 💡 Lightning Talks: Bring your own 5-minute talk or pitch an idea!
- 🍕 Networking with drinks, pizza, and plenty of discussions about AI and tech.
This is your opportunity to meet other data science professionals from the Nuremberg area, sharpen your skills and exchange ideas with like-minded professionals.
This time, we're back at Codecentric. Big thanks for hosting us again!
Agenda:
- 18:00 Welcome & Intro with Lukas Spranger
- 18:15 Dr. Konstantin Hopf: Crafting AI. The tension between the craftwork of data scientists and the managerial perspective
- 19:15 Lightning Talks (bring your own short talk or pitch!)
- Networking with pizza and drinks
Talks:
(German below)
Dr. Konstantin Hopf: Crafting AI. The tension between the craftwork of data scientists and the managerial perspective
Abstract: Data science is one of the fastest growing professional fields of our time. Data scientists are the makers of AI applications, the people behind automation. In practice, however, we see many data science and AI projects fail or fail to deliver the expected business value. This leads to frustration, not only in the data science profession.
What are the biggest challenges of data science and AI projects in practice and how can they be effectively addressed?
The talk will address five major challenges of data science projects. It will show the tension between the mechanistic-technical expectation of data science/AI applications and the craft-like nature of data science work as the cause of these challenges – an insight from a scientific interview study with >100 data scientists. The talk will describe 15 tactics for the effective management of data science projects. This knowledge also helps data scientists, developers, ML engineers and other professions in the AI environment with their self-image and expectation management.------
Data Science ist eines der am schnellsten wachsenden Berufsfelder der Zukunft. Data Scientists sind die Macher von KI-Anwendungen, die Menschen hinter der Automatisierung. In der betrieblichen Praxis sehen wir jedoch viele Data-Science- und KI-Projekte scheitern oder nicht den erwarteten Geschäftswert liefern. Das führt zu Frustration, nicht nur in der Data-Science-Berufsgruppe.
Was sind die größten Herausforderungen bei Data-Science- und KI-Projekten in der Praxis und wie kann man diesen effektiv entgegentreten?
Der Vortrag geht auf die fünf größten Herausforderungen von Data-Science-Projekten ein. Er wird die Spannung zwischen der mechanistisch-technischen Erwartung an Data-Science-/KI-Anwendungen und den handwerklichen Charakter der Data-Science-Arbeit als Ursache für diese Herausforderungen aufzeigen—eine Erkenntnis aus einer wissenschaftlichen Interviewstudie mit >100 Data Scientists. Der Vortrag beschreibt 15 Taktiken für das effektive Management von Data-Science-Projekten. Dieses Wissen hilft auch Data Scientists, Entwicklern, ML-Engineers und anderen Berufen im KI-Umfeld bei ihrem Selbstverständnis und dem Erwartungsmanagement.
