This semester, I—as a teacher—came across two cases of heavily reliance on AI by master students, mostly for coding purposes, to which I had rather surprisingly not been exposed before. (Except for this plagiarised thesis two years ago that essentially rewrote existing papers with synonyms and for which we had to get to the disciplinary committee!) One project made a massive advance within two days, with hundreds of lines of beautiful python code, and reasonable output, but with my student unable to explain the code or the method behind… And anther case homeworks involving coding came back with extremely clean codes as well. Meaning they could not be graded and we had to switch to another type of evaluation. Oh well, welcome ol’me into the new age (just for a few years!)
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Posted in Books, Kids, pictures, Statistics, University life with tags ChatGPT, coding, debugging, discipline, master project, plagiarism, Python, R, teaching, Université Paris Dauphine, University of Warwick on November 12, 2025 by xi'anStatistics versus Data Science [or not]
Posted in Books, Kids, Statistics, University life with tags data science, discipline, fashion, future, machine learning, Statistics, Toronto, trend, University of Warwick on October 13, 2017 by xi'an
Last week a colleague from Warwick forwarded us a short argumentation by Donald Macnaughton (a “Toronto-based statistician”) about switching the name of our field from Statistics to Data Science. This is not the first time I hear of this proposal and this is not the first time I express my strong disagreement with it! Here are the naughtonian arguments
- Statistics is (at least in the English language) endowed with several meanings from the compilation of numbers out of a series of observations to the field, to the procedures proposed by the field. This is argued to be confusing for laypeople. And missing the connection with data at the core of our field. As well as the indication that statistics gathers information from the data. Data science seems to convey both ideas… But it is equally vague in that most scientific fields if not all rely on data and observations and the structure exploitation of such data. Actually a lot of so-called “data-scientists” have specialised in the analysis of data from their original field, without voluntarily embarking upon a career of data-scientist. And not necessarily acquiring the proper tools for incorporating uncertainty quantification (aka statistics!).
- Statistics sounds old-fashioned and “old-guard” and “inward-looking” and unattractive to young talents, while they flock to Data Science programs. Which is true [that they flock] but does not mean we [as a field] must flock there as well. In five or ten years, who can tell this attraction of data science(s) will still be that strong. We already had to switch our Master names to Data Science or the like, this is surely more than enough.
- Data science is encompassing other areas of science, like computer science and operation research, but this is not an issue both in terms of potential collaborations and gaining the upper ground as a “key part” in the field. Which is more wishful thinking than a certainty, given the existing difficulties in being recognised as a major actor in data analysis. (As for instance in a recent grant evaluation in “Big Data” where the evaluation committee involved no statistician. And where we got rejected.)
