2025-04-25 –, Palladium
As a data scientist, I value the power of insightful visualizations to unlock unique interpretations of complex data. In my talk, I will introduce an elegant mathematical framework called Formal Concept Analysis (FCA), developed in the 1980s in Darmstadt.
FCA transforms binary data into concepts that can be visualized as a hierarchical graph, offering a fresh perspective on multidimensional data analysis. Leveraging this theory and its open-source Python libraries, I am developing an interactive Dash-based tool featuring interactive tables and graphs to explore data insights.
To illustrate its potential, I will showcase an optimization of the entire tariffing system of an energy provider company, highlighting how FCA can bring structure and clarity to even such tangled datasets.
My goal is to introduce Formal Concept Analysis (FCA) as a fascinating mathematical framework. I aim to inspire Python enthusiasts to explore its potential and uncover insights in their data analysis tasks. The talk is divided into three sections:
1 FCA Basics
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What is a "concept"? First, I am going to introduce the main terms used in FCA and define the central object of the theory - the formal concept.
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Illustrative example. To show the power of FCA in action, I will provide a relatable example to explain the hierarchical structure of the graph visualization.
2 Python Implementation
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fcapyPython library. Core functionality overview of the library and the data formats it can use. -
Introducing interactivity with Python Dash: Enhancing exploration and user experience with interactive tables (AG Grid) and dynamic graph visualizations (Cytoscape).
3 Applications and Practical Relevance
- Use Case: Energy Tariffing System Optimization. In this section, I am going to showcase the real data in its original complexity and the optimization process of identifying redundancies, overlaps, or inefficiencies.
- Examples of other applications and key takeaways
Intermediate
Expected audience expertise: Python:Intermediate
After my PhD in Astronomy @ Max Planck Institute for Astronomy in Heidelberg, I have switched from academia to industry. Working as a Data Scientist @ DSC GmbH I am developing in python for various projects including those involving language models. I am attending PyData meetings in Heidelberg and even presented a lightning talk on my "Croshapes" hobby project.