This is the second part of my reaction to this post by Kai, a fellow PhD Student at Blekinge TH and Ericsson AB. I had already introduced his blog and you can find the previous part in the previous post on this blog. In this post he reports among others about two other courses he has to take besides from software productivity. I will try to shortly analyze what he wrote about scientific publications and statistical methods.

About scientific publications, what interested me is that they get to study scientometrics. Scientometrics, according to Kai, are the methods used to assess the relevance and importance of journals and scientific conferences. If you have worked as a researcher you must know that not all scientific conferences, workshops, symposia, journals and all sorts of scholarly transactions are equal. They surpass each other in terms of popularity, reach, perceived quality and influence on the scientific community. Using Scientometrics, the impact factors of journals are established, which helps the researcher to select where he wants his papers to be published. as Kai simply expresses it: if you get a paper accepted on one of the journals with the highest impact factors for your community and area of research, then " this increases your reputation as a scientist in the area". But logically, the difficulty to get a paper accepted at one of these conferences grows with their impact factor.

What they learn in this course is to asses the impact factors of target journals and conferences, the major scientists (what my supervisor calls immediate research community), to come up with a strategy of publication and to make a review of papers. I guess the latter concerns papers that have been accepted at famous scholarly transactions or that have been written by members of the major scientists in the community.

Knowing the major scientists in the community is very important. First of all you get to know the most important directions of research, and you get to know the most important results already achieved. So you get a lot quicker to know the state of the art of the area you are working on. Also studying the references used by these scientists can help you know the foundations of your research area that you may want to read for a solid and fundamental understanding of current state of the art results. these scientists are the ones that publish the most articles and the ones at the most important transactions. These scientists normally also show you at which journals and conferences papers tackling related topics can be published and which ones are the most relevant.

The second course are statistical methods. Now, like every computer scientist I have had my share of statistical mathematics. Actually more, due to the emphasis put onto mathematics and the additional statistics options I took at my engineering school, the ENSIMAG, Grenoble. But this is not what Kai is talking about. He specifically says that they take a course dedicated to learn how to use statistics to evaluate and analyze data gathered during research (especially in software engineering, the use of case studies as qualitative analysis tools is widely done, as I can see it from the work of Sebastian). Such use cases and the accompanying statistical analysis can be of great use when tackling one of the last steps of a PhD, which is evaluating your results. The course is based on concrete problems, in 5 seminars.

The goal of these two posts was to give an idea about how a structured PhD at a university (at least partly) takes on the harsh project of a dissertation, and the tools PhD students get to learn and to use. In a totally industrial PhD, you have to try to go as structured as possible with the tools that you have. That means you shouldn't expect to have time, resources to learn or mentors who will teach you how to optimally get on with your PhD. You are in quite some extent an auto-didact, a multi-disciplinary researcher and necessarily one with extended curiosity. The desire to work in a highly structured way and the ability to combine several sources of information, from areas that do not necessarily have much to do with your direct area of research, is a must. being open to learn from the techniques of others and to get the best out of all who you meet or you read about can only make your PhD better.

I hope that my two small analyses have helped better explain what an industrial PhD is about. I will write some other posts about this, since I have quite some opinions to share concerning the topic.

Marwane El Kharbili

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