Ludwigshafen University of Business and Society
LUMEN is a student-driven initiative dedicated to bridging the gap between academic theory and the fast-paced reality of the tech industry. We empower students to become the next generation of data leaders through hands-on projects, workshops, and industry collaboration.
We believe that data literacy is the literacy of the 21st century. Our mission is to democratize data science and make it accessible to everyone, regardless of their background. We foster a community of innovation, excellence, and collaboration.
We operate on two main tracks to cater to different career paths and interests:
Focuses on skill acquisition, practice, and networking.
- Education & Academy: Bootcamps (Data Science 101), Tool Workshops (PowerBI, Cloud, SQL), and Paper Reading Groups.
- Practice & Competition: Internal Hackathons, Kaggle Nights, and Data Dives (fun portfolio projects).
- Career & Networking: Company Visits (BASF, SAP), Fireside Chats with experts, and CV Checks.
Focuses on real-world application and delivering value to clients.
- Strategy & Consulting: Data Maturity Assessments, Use Case Workshops, and Tool Evaluation.
- Business Intelligence & Analytics: Dashboarding, Process Automation, and Process Mining.
- Data Science & AI Development: Rapid Prototyping (MVPs), Customer Analytics, and Web Scraping.
Explore some of the open-source projects and initiatives driven by our community:
- LUMEN Website: The official website you are looking at right now. View on GitHub
- DataViz Dashboard: Interactive dashboard for visualizing complex datasets.
- NLP Sentiment Analyzer: Machine learning model for social media sentiment analysis.
- Predictive Maintenance: IoT solution for predicting equipment failures.
- Computer Vision API: REST API for object detection and image classification.
This website is built with:
- Framework: Next.js 14 (App Router)
- Styling: CSS Modules & Global CSS (Glassmorphism, Dark Theme)
- Deployment: GitHub Pages
First, run the development server:
npm run devOpen http://localhost:3000 with your browser to see the result.