Data Science

What are the necessary attitudes? Almost all the most vital attitudes can be described in a type form: willingness to face up to X. Granted that facing up can be uncomfortable, history suggests it is possible.
We need to face up to more realistic problems. The fact that normal theory, for instance, may offer the only framework in which some problem can be tackled simply or algebraically may be a very good reason for starting with the normal case, but never can be a good reason for STOPPING there. We must expect to tackle more realistic problems than our teachers did, and expect our successors to tackle problems which are more realistic than those we ourselves dared to take on.
We need to face up to the necessarily approximate nature of useful results in data science. Our formal hypotheses and assumptions will never be broad enough to encompass actual situations. Even results that pretend to be precise in derivation will be approximate in application. Consequently we are likely to find that results which are approximate in derivation or calculation will prove no more approximate in application than those that pretend to be precise, and even that some admittedly approximate results will prove to be closer to fact in application than some supposedly exact results.
We need to face up to the need for collecting the results of actual experience with specific data-analytic techniques. Mathematical or empirical-sampling studies of the behavior of techniques in idealized situations have very great value, but they cannot replace experience with the behaviour of techniques in real situations.
We need to face up to the need for iterative procedures in data science. It is nice to plan to make but a single analysis, to avoid finding that the results of one analysis have led to a requirement for making a different one. It is also nice to be able to carry out an individual analysis in a single straightforward step, to avoid iteration and repeated computation. But it is not realistic to be lieve that good data science is consistent with either of these niceties. As we learn how to do better data science, computation will get more extensive, rather than simpler, and reanalysis will become much more nearly the custom.
We need to face up to the need for both indication and conclusion in the same analysis. Appearances which are not established as of definite sign, for example, are not all of a muchness. Some are so weak as to be better forgotten, others approach the borders of establishment so closely as warrant immediate and active following up. And the gap between what is required for an interesting indication and for a conclusion widens as the structure of the data becomes more complex.
We need to face up to the need for a free use of ad hoc and informal procedures in seeking indications. At those times when our purpose is to ask the data what it suggests or indicates it would be foolish to be bound by formalities, or by any rules or principles beyond those shown by empirical experience to be helpful in such situations.
We need to face up to the fact that, as we enter into new fields or study new kinds of procedures, it is natural for indication procedures to grow up before the corresponding conclusion procedures do so. In breaking new ground (new from the point of view of data science), then, we must plan to learn to ask first of the data what it suggests, leaving for later consideration the question of what it establishes. This means that almost all considerations which explicitly involve probability will enter at the later stage.
We must face up to the need for a double standard in dealing with error rates, whether significance levels or lacks of confidence. As students and developers of data science, we may find it worth while to be concerned about small difference among error rates, perhaps with the fact that a nominal 5 % is really 4 % or 6 %, or even with so trivial a difference as from 5 % to 4.5 % or 5.5 %. But as practitioners of data science we must take a much coarser attitude toward error rates, one which may sometimes have difficulty distinguishing 1 % from 5 %, one which is hardly ever able to distinguish more than one intermediate value between these conventional levels. To be useful, a conclusion procedure need not be precise. As working data scientists we need to recognize that this is so.
We must face up to the fact that, in any experimental science, our certainty about what will happen in a particular situation does not usualy come from directly applicable experiments or theory, but rather comes mainly through analogy be tween situations which are not known to behave similarly. Data science has, of necessity, to be an experimental science, and needs therefore to adopt the attitudes of experimental science. As a consequence our choices of analytical approach will usually be guided by what is known about simpler or similar situations, rather than by what is known about the situation at hand.
Finally, we need to give up the vain hope that data science can be founded upon a logico-deductive system like Euclidean plane geometry (or some form of the propositional calculus) and to face up to the fact that data science is intrinsically an empirical science. Some may feel let down by this, may feel that if data science cannot be a logico-deductive system, it inevitably falls to the state of a crass technology. With them I cannot agree. It will still be true that there will be aspects of data science well called technology, but there will also be the hallmarks of stimulating science: intellectual adventure, demanding calls upon insight, and a need to find out "how things really are" by investigation and the confrontation of insights with experience. -- John Tukey (The Future of Data Analysis) with minor modification by Hatef Monajemi