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Principles of Data Science: Primer

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Book cover Data Driven

Part of the book series: Management for Professionals ((MANAGPROF))

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Abstract

Let us face it. Statistics and mathematics deter almost everyone except the ones who choose to specialize in it. If you kept reading and reached this far in the book you are probably now considering skipping the chapters on Data Science and moving on to the next on Strategy because, well, it sounds more exciting. Thus, let us start this chapter on statistics by a simple example that illustrates why it is worth reading and why consultants may increasingly use mathematics.

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Notes

  1. 1.

    One may recommend “Naked Statistics” from Charles Wheelan [89], which introduces the overall field of statistics in a simple and humoristic way …technical expertise not required.

  2. 2.

    The software-hardware interface defines the field of Robotics as an application of Cybernetics, a field invented by the late Norbert Wiener and from where Machine Learning emerged as a subfield.

  3. 3.

    Pearson correlation is the most common in loose usage.

  4. 4.

    By general purpose, I mean the assumption of linear relationship between variables, which is often what is meant by a “simple” model in mathematics.

  5. 5.

    Eq. 6.5 is formally the divergence of p2 from p1. An unbiased degree of association according to Kullback and Leibler [157] is obtained by taking the sum of each one-sided divergence: D(p 1 ,p 2 ) + D(p 2 ,p 1 ).

  6. 6.

    All 1-dimentional values in mathematics are referred to as scalars; multi-dimensional objects may bear different names, most common of which are vectors, matrices and tensors.

  7. 7.

    Hyperspace is the name given to a space made of more than three dimensions (i.e. three variables). A plane that lies in a hyperspace is defined by more than two vectors, and called a hyperplane. It does not have a physical representation in our 3D world. The way scientists present “hyper-“objects such as hyperplanes is by presenting consecutive 2D planes along different values of the 4th variable, the 5th variable, etc. This is why the use of functions, matrices and tensors is strictly needed to handle computations in multivariable spaces.

  8. 8.

    As mentioned in Sect. 6.1, the standard error is the standard deviation of the means of different sub-samples drawn from the original sample or population

  9. 9.

    The 80/20 rule, or Pareto principle, is a principle commonly used in business and economics that states that 80% of a problem stem from only 20% of its causes. It was first suggested by the late Joseph Juran, one of the most prominent management consultants of the twentieth century.

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Authors and Affiliations

  1. Amazon Web Services, Inc, New York, NY, USA

    Jeremy David Curuksu

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  1. Jeremy David Curuksu
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Curuksu, J.D. (2018). Principles of Data Science: Primer. In: Data Driven. Management for Professionals. Springer, Cham. https://doi.org/10.1007/978-3-319-70229-2_6

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