Data Morgana: The Advent of Data Science, a contemporary Euthyphro Dilemma?
Data Morgana is a short lecture series, divided into four parts, about the promises, hype, and misconceptions surrounding data science. Join us at this first session, where we ask the question: does the advent of data science pose a contemporary Euthyphro dilemma? (English / SG-Certificate*)
Time: 16:00-16:45 hrs.
Join this event online via Zoom
- Meeting ID: 929 7854 6226
- Passcode: 0204600093
Data is the new gold! Does that mean data scientists are the new alchemists? In this short lecture series, we discuss the role of data science in general scientific practice, and in our global society. We look beyond the hype, beyond the mirage of unlimited promises and beyond hollow buzzwords. What have we accomplished with data science; what has it cost us; and what can we actually expect from it in the future? In this first session, our focus is on the Advent of Data Science.
A Whole New World?
The origins of data science are hard to pinpoint, without using a common definition of data science. If we look at data science as a profession, one could argue that ‘data scientists’ have only really started popping up at every mid-large company in the past ten years or so. But, if we see data science as a scientific extension of data analysis and statistics as it has been practiced for decades, the advent of data science may even take us back to early in the 20th century. To kick off our ‘Data Morgana’ series, we must first come to a common understanding of the origins of data science, and what makes data science essentially different from its predecessors in scientific methodology. Then we can hopefully begin to grasp the effect data science and its development so far has had on our society.
Dr. Richard Starmans is managing director of The Netherlands Research School for Information and Knowledge Systems (SIKS). He is also an Associate Professor at the Tilburg School of Humanities and Digital Sciences (Tilburg University).
His research and publications focus primarily on the intersection between philosophy and science, specifically mathematical statistics, data science and artificial intelligence. In the “Handbook of Big Data” (Buelmann et al, Chapman & Hall/CRC, New York, 2016), Starmans wrote the chapter on the foundations of data science titled: “The Advent of Data Science – some considerations on the unreasonable effectiveness of data”.