# Short Datathon for the Interdisciplinary Development of Data Analysis and Visualization Skills

## TL;DR

This paper introduces the Short Datathon, a collaborative event format aimed at developing data analysis and visualization skills across disciplines, demonstrated through case studies on tax evasion with positive participant feedback.

## Contribution

It presents a novel Short Datathon format for interdisciplinary data analysis skill development and evaluates its effectiveness through real-world case studies.

## Key findings

- Participants improved understanding of data analysis and visualization.
- Interdisciplinary collaboration enhanced data analysis efficiency.
- Participants valued the event for practical learning and teamwork.

## Abstract

Understanding the major fraud problems in the world and interpreting the data available for analysis is a current challenge that requires interdisciplinary knowledge to complement the knowledge of computer professionals. Collaborative events (called Hackathons, Datathons, Codefests, Hack Days, etc.) have become relevant in several fields. Examples of fields which are explored in these events include startup development, open civic innovation, corporate innovation, and social issues. These events have features that favor knowledge exchange to solve challenges. In this paper, we present an event format called Short Datathon, a Hackathon for the development of exploratory data analysis and visualization skills. Our goal is to evaluate if participating in a Short Datathon can help participants learn basic data analysis and visualization concepts. We evaluated the Short Datathon in two case studies, with a total of 20 participants, carried out at the Federal University of Technology - Paran\'a. In both case studies we addressed the issue of tax evasion using real world data. We describe, as a result of this work, the qualitative aspects of the case studies and the perception of the participants obtained through questionnaires. Participants stated that the event helped them understand more about data analysis and visualization and that the experience with people from other areas during the event made data analysis more efficient. Further studies are necessary to evolve the format of the event and to evaluate its effectiveness.