In this section of the blog, I discuss topics related to data science, AI, and academia.
Posts on software engineering
AWS (Amazon Web Services) certifications are among the most lucrative certifications in the IT sector. This is due to the growing demand for professionals with cloud expertise, as more and more companies are adopting cloud technology. Furthermore, AWS upholds high quality standards when it comes to certification. So, while certification can be challenging, there is a lot to learn along the way. I only recently had my first exposure to cloud computing when I took on a DevOps role in industry in 2019.
The Cambridge Dictionary defines plagiarism as ‘the process or practice of using another person’s ideas or work and pretending that it is your own’. In the last years, there have been several famous Germans who lost their PhD titles due to plagiarizing their doctoral theses. In Germany, VroniPlag is the largest open community that analyzes scientific work with respect to plagiarism. Most notably, in 2011, Guttenplag (a specific group of plagiarism hunters) published a detailed analysis of the doctoral thesis by Karl-Theodor zu Guttenberg, the German defense minister at that time.
A PhD is not only a test of professional aptitude but also a test of character. Looking back at my time as a PhD student, I can say that it has been a taxing but equally rewarding time that I wouldn’t exchange for anything in the world. Doing a PhD has not only improved my scientific and technical understanding but has also strengthened my character. In this post I describe five characteristics that I found to be helpful in successfully completing my PhD.
Having recently transitioned from academia to industry, I’d like to share what I found are the greatest differences between working in industry and academia. Since this article is based on my personal experiences, I would first like introduce my respective roles in research and in industry. After that, I will summarize the main differences between industry and academia. Finally, I offer some pieces of advice regarding how to prepare for an industry job when transitioning from academia.
Having obtained both a Bachelor’s and a Master’s degree in bioinformatics, I would like to describe how I experienced studying bioinformatics. Moreover, I would like to discuss whether it was worth studying in the first place, and, finally, to offer some advice to prospective students and graduates. What is Bioinformatics? Bioinformatics is an interdisciplinary field that is concerned with developing and applying methods from computer science on biological problems.
During my time as a PhD student I have developed software in the academic setting. At that time I was already under the impression that my work would probably not meet industry standards. Having recently transitioned to an industry job, I quickly realized how coding in academia is different from coding in industry. This post summarizes the main differences between the two fields and extrapolates what coders in academia can learn from industry.
The terms inference and prediction both describe tasks where we learn from data in a supervised manner in order to find a model that describes the relationship between the independent variables and the outcome. Inference and prediction, however, diverge when it comes to the use of the resulting model: Inference: Use the model to learn about the data generation process. Prediction: Use the model to predict the outcomes for new data points.