Page 66 - Special Topic Session (STS) - Volume 3

STS515 Jim R. et al.
5. Concluding Remarks
The early history of data science holds important lessons; technology,
mathematics and society are in a continuous state of rapid change. Students
should be made aware that current knowledge will be superseded, and that
there are social forces that can limit their creativity.
Technologies have created existential threats to humanity such as global
warming and nuclear war. There is a pressing urgency to address such
problems. Both statistics and data science have their roots in solving
challenging problems, but have traditionally adopted somewhat different
approaches. Statistics is characterised by sophisticated modelling using a
small set of well-defined variables; data science is often a-theoretical. Data
science adopts practices that should be applied across a wide range of
disciplines, such as sharing data, code and workflows. Statistics is strong on
discovery methods.
There is an urgent need to create an Epistemological Engine – a set of
semi-automated tools to understand and support effective science.
Statisticians and data scientists are the people best placed to create and
maintain this Engine. We offer some ideas on the tool set that will comprise
the EE, and some suggestions about the competences needed by future data
wranglers.
And a final piece of advice for young minds: make a wall poster of these
words from Ada Augusta King, Countess of Lovelace...
“A new, a vast, and a powerful language is developed for the future of
analysis... the theoretical and the practical in the mathematical world, are
brought into more intimate and effective connexion with each other.”
(Lovelace, 1843, p3)

References
1. Babbage (1864). Passages from the Life of a Philosopher.
(https://en.wikisource.org/wiki/Passages_from_the_Life_of_a_Philosophe
r/Chapter_VIII
2. Boole, G. (1854). The Laws of Thought. An Investigation of The Laws of
Thought on Which are Founded the Mathematical Theories of Logic
and Probabilities, Originally published by Macmillan, London. Reprint by
Dover, 1958. Cited at
https://plato.stanford.edu/entries/boole/#LawsThou1854
3. Box, G., and Draper, N. (1987). Empirical Model-building and Response
Surfaces. New York: Wiley. Breiman, L. (2001). Statistical modeling: the
two cultures (with comments and a rejoinder by the author). Stat. Sci.,
16(3), 199–231.

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