Data Drive

MINDtastic Club Data Drive

Learning in communities within a practical learning club format on the topics of big data, AI and deep learning.

The Club Mentors

The Julius Maximilians University, Würzburg
PH.D. Computer Science
Johannes Keppler Universität, Linz
Dipl. Ingenieur, Industrial Mathematics & B.Sc. Technical Math

2022 – ongoing
TGW Logistics Group, Manager of Artificial Intelligence Competences
2019 – 2022
STIWA Group, Data Scientist
2017 – 2019
IBM, Data Scientist

Content

Basics of Collective AI Development vs. Industrial Domain Specific Development
Do’s and Don’ts when dealing with big data and data engineering
Insight into the conception of data driven products – presentation of two methodologies
Introduction to the basic technical knowledge of data driven approaches
Explanation of terms and concepts:
- Supervised learning, unsupervised learning, reinforcement learning, regression, decision tree
- Insight into hypothesis testing and general statistics
Possible applications of big data in combination with AI or without
- Anomaly Detection
- Predictive Maintenance
- Which methods are sufficient to solve a problem but without AI
- Time series analysis
- Image recognition and computer vision (Rovolution, Rovoflex, Autonomous Driving)
- LLMs opportunities and risks for data-driven applications

Goals

Developing a deep understanding of data-driven solutions
More extensive knowledge of the different sub-forms of artificial intelligence and machine learning
Differentiation competence of AI algorithms versus non-AI algorithms
Technical understanding & methodical application skills when designing data-driven concepts
Establishment of a permanent, self-organized learning club on data-driven solution approaches
Additional goal: Learning club has a long-term impact across the entire TGW to spread understanding and application skills on big data, AI and deep learning