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How to become a software developer in science?


My path from 'Hello world' to software development was long and hard. The approach I learned during my research may help you to create high quality software and improve as a developer. The talk covers how you can benefit from your non-IT knowledge, atomize your project and how collaboration accelerates your learning.


Goal: give practical tools for improving skills and software quality to people with a background other than IT.

Eight years ago, as a plant biologist, I knew almost nothing about programming. When I took a course in python programming, I found myself so fascinated that it altered my entire career. I became a scientific software developer. It was long and hard work to get from the level of 'Hello world' to the world of software development. The talk will cover how to embrace a non-IT education as a strength, how and why to atomize programming tasks and the importance of doing side projects.

1. Embrace your background

Having domain specific knowledge from a field other than IT helps you to communicate with the team, the users and the group leader. It prevents misunderstandings and helps to define features better. A key step you can take is systematically apply the precise domain specific language to the code e.g when naming objects, methods or functions. Another is to describe the underlying scientific process step by step as a Use Case and write it down in pseudocode.

2. Atomisation

Having a set of building block in your software helps to define responsibilities clearly. Smaller parts are easier to test, release and change. Modular design makes the software more flexible and avoids the Blob and Lava Flow Anti-Patterns. When using object oriented programming a rule of thumb is that an object (in Python also a method) does only one thing. You can express this Single Responsibility Principle as a short sentence for each module. Another practical action is to introduce Design Patterns that help to decouple data and its internal representation. As a result, your software becomes more flexible.

3. Participating in side projects

Learning from others is a great opportunity to grow. Through side projects you gain a fresh perspective and learn about best practices in project management. You gain new ideas for improvement and become aware of difficulties in your own project. You can easily participate in a scientific project by adding a small feature, writing a test suite or provide a code review on a part of a program.

Summarizing, in scientific software development using domain-specific knowledge, atomisation of software, and participation in side projects are three things that help to create high quality software and to continuously improve as a developer.

The talk will address challenges in areas where science differs from the business world. It will present general solution one might use for software developed in a scientific environment for research projects rather then discussing particular scientific packages.


During my PhD I developed a software on 3D RNA modeling ( that resulted in 7 published articles. I am coauthor on a paper on bioinformatic software development. Currently I am actively developing a system biology software in Python at the Humboldt University Berlin (


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