The concept of the hackathon comes originally from the hardware and software development and designates events in which complex problems are to be solved in the best possible way in a relatively short time (usually in the range of about half to about three days). The participants of a hackathon ideally form interdisciplinary teams, in which they work together on the task. The competitive situation and the time pressure should promote the development of new and creative solutions. Depending on the size and type of hackathon, the solutions developed at the end of the event will be judged by a jury and, if applicable, awarded. For the context of the CorrelAid Local Chapter, hackathons are a great way to give data science-interested students first-hand insights into practical data analysis.

The first step in planning a hackathon is the question of the scope and format of the event:

  • Should there be a standard task for all participants, or should several projects be available in different difficulty stages?

  • How long should the hackathon last and when should it take place? Are there any university events to which the hackathon can be attached?

  • How many participants * are expected?

Once you have answered these basic questions, you can get into the specific organization: As always, a room should be booked early for the hackathon. This should ideally have a free seating and a large number of sockets. For larger events, it is advisable to book two rooms, otherwise the noise level can become too high. In hackathons, it is customary that the organizers provide a certain basic set of drinks (coke, mate, water, beer ..) and food (pizza). At multi-day events, the workrooms are usually open around the clock and full catering is available. For examples regarding catering see section (Structure of the Local Group - Catering).

The most important success criterion of a hackathon is the selection of a good task. Ideally, it is a "real life" problem, so the solutions found may even be put to practical use later. For example, potential issues may arise during a data dialogue or may be based on other, freely available data (open government, etc.). However, it is important that no sensitive / personal data is used on a hackathon. Examples of tasks of successful hackathons:

  • Analysis of data of the Bundesfernstraßengesellschaft on the impact of the blockage of the Rheintalbahn on the truck traffic.

  • Analysis of a data dump from FlightRadar to identify violations of the no-fly ban at German airports.

  • Identification of hacker attacks in network logs.

In addition, it would be helpful if contact persons familiar with the data are constantly available during the hackathon and can help with questions and problems.

  • Responsible

  • Timeframe

  • Work steps

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