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From a recent conversation in a typical local pub: “How on earth can Google be worth almost ten times the value of every German car maker? It produces nothing!” Even colleagues from the German software industry, who were also present in the bar, thought the whole thing was merely hype. “Ultimately, what really matters is what you can touch or see!”

It is this kind of attitude that makes it so difficult to introduce Data Science, i.e., the science of information processing and analysis, as an independent discipline. Who would choose a degree course on the basis of hype – to produce nothing tangible in the end? The German idea of a perfect son- or daughter-in-law seems to be quite different. He or she would surely be an engineer, producing or building something real.

Let us look at this from another angle. What is the foundation of a real, solid market? This might be stretching the point a bit, but I don’t really need a car. I just want to get from A to B as quickly, conveniently and cheaply as possible, so a car is, in fact, only a means to an end. On the other hand, information is something we will always need. It is not a means, but an end in itself. Whether I am interested in reviews of a certain pizzeria in an unknown town, statistics on the most popular type of pizza (which is salami), or want to know the latest local news, I am exhibiting innate human inquisitiveness – even gossip is natural. I can imagine a person who does not own a car but not someone who can do without information or gossip. The need for a car might be considered hype – admittedly a long and enduring story – but we cannot say that about the need to process information. Germany’s perfect son- or daughter-in-law should study data science!

But don’t get me wrong: There is no doubt that automotive engineering is an absolutely future-proof and interesting field of study. Yet, it would be a mistake in Germany not to consider data science as a central study discipline as well and not to promote it accordingly. In other words, we should have separate Bachelor’s and Master’s degree programs in data science rather than offering it only as a supplementary course or sub-topic of informatics or engineering. Handling data requires a specific approach that a student learns best in an independent degree course with a clear focus. Such courses should clarify, for instance, which analytical methods are best suited to which areas of application, how important the issue of data security or social acceptance is and how big volumes of data can be efficiently stored and processed. Each study course should also facilitate insights into different fields of application to ensure that data scientists are able to work together with experts from other disciplines and application areas.

Without suitably qualified data scientists, Germany could miss out on new markets: only professionals can establish data analysis startups. If companies, e.g., from the production, construction and mechanical engineering sector, are to implement new technologies in line with Industrie 4.0 or artificial intelligence, they will need a labour market that offers the requisite skilled employees. These markets are already emerging – with or without Germany.

The German government has already steppedup its support of research in the field of AI. What we need next are investments in broad-based education in data science. The appropriate training must be offered in form of standard degree programs at most colleges and universities. Currently, no more than 10 to 20 such Bachelor’s programs are available in Germany, in spite of more than 300 colleges and universities in the country. Data science is neither information technology nor an engineering science, but a new and promising key discipline in its own right. It is one of those trends we cannot afford to ignore!