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Marketing Meets Data Science: Bridging the Gap

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Business and Consumer Analytics: New Ideas

Abstract

It is certain that computer science is completely reformulating the way that business is being conducted around the world. We are witnessing the increasing availability of large volumes of data together with the advances in artificial intelligence, machine learning and optimization techniques. Breakthroughs in statistics, discrete applied mathematics and new algorithms are leading to the development of a new interdisciplinary field: data science. The purpose of this chapter is to provide a bridge, a short-cut to understand some of the questions that computer science deals with in a context of developing new techniques to get knowledge from data.

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Notes

  1. 1.

    https://www.youtube.com/watch?v=qtboCGd_hTA.

  2. 2.

    https://www.turing.ac.uk/.

  3. 3.

    http://www.chilton-computing.org.uk/inf/transputers/p011.htm.

  4. 4.

    http://energy.gov/articles/new-titan-supercomputer-named-fastest-world.

  5. 5.

    https://ec.europa.eu/commission/priorities/digital-single-market_en.

  6. 6.

    https://www.gov.uk/government/publications/the-age-of-algorithms.

  7. 7.

    http://www.businessnewsdaily.com/5450-internet-of-things-business-opportunities.html.

  8. 8.

    https://en.wikipedia.org/wiki/Big_data.

  9. 9.

    http://www.ibmbigdatahub.com/infographic/four-vs-big-data.

  10. 10.

    Proof That Computers Can’t Do Everything, (The Halting Problem) available in YouTube at: https://www.youtube.com/watch?v=92WHN-pAFCs.

  11. 11.

    http://bactra.org/notebooks/complexity-measures.html.

  12. 12.

    Problems for which the answer can only be either “Yes” or “No” are called decision problems.

  13. 13.

    Millennium Prize: P vs. NP http://theconversation.com/millennium-prize-p-vs-np-4246.

  14. 14.

    En passant, an area of computer science that is concerned with visualization algorithms, creates aesthetically and perceptually informative layouts of data structures such as graphs. Chapter 16 presents one approach and several references can help the reader to have an introduction to the topic.

  15. 15.

    http://geneticprogramming.com/software/.

  16. 16.

    http://www.nutonian.com.

  17. 17.

    https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/.

  18. 18.

    http://archive.boston.com/news/nation/articles/2009/02/18/chapter_4_sailing_into_the_wind/.

  19. 19.

    http://www.nbcnews.com/id/5612836/ns/politics/t/mccain-deplores-anti-kerry-ad/.

  20. 20.

    http://www.businessinsider.com.au/john-kerry-donald-trump-john-mccain-2015-7.

  21. 21.

    http://michaelmoore.com/trumpwillwin/

  22. 22.

    http://analytics-magazine.org/13-keys-to-the-white-house/.

  23. 23.

    http://www.newyorker.com/magazine/1958/12/06/rival-2.

  24. 24.

    https://en.wikipedia.org/wiki/Quadric.

  25. 25.

    http://quoteinvestigator.com/2015/07/23/great-power/.

  26. 26.

    http://www.scholarpedia.org/article/Neocognitron.

  27. 27.

    http://fortune.com/ai-artificial-intelligence-deep-machine-learning/.

  28. 28.

    http://bactra.org/notebooks/complexity-measures.html.

  29. 29.

    http://www.csie.ntu.edu.tw/~htlin/paper/doc/wskdd10cup.pdf.

  30. 30.

    http://www.csie.ntu.edu.tw/~cjlin/courses/dmcase2010/slide.pdf.

  31. 31.

    http://calteches.library.caltech.edu/3043/1/CargoCult.pdf.

  32. 32.

    https://work.caltech.edu/lecture.html.

  33. 33.

    https://www.britannica.com/topic/Occams-razor.

  34. 34.

    https://en.wikiquote.org/wiki/William_of_Ockham.

  35. 35.

    http://www.scholarpedia.org/article/Sampling_bias.

  36. 36.

    https://en.wikipedia.org/wiki/Simpson's_paradox.

  37. 37.

    https://en.wikipedia.org/wiki/Waist-to-height_ratio.

  38. 38.

    http://www.npr.org/templates/story/story.php?storyId=106268439.

  39. 39.

    https://en.wikipedia.org/wiki/Support_vector_machine.

  40. 40.

    http://neuralnetworksanddeeplearning.com.

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Acknowledgements

The authors would like to thank Luke Mathieson, Ivana Ljubic and Shannon Fenn for helpful comments on early drafts of this chapter. Pablo Moscato acknowledges previous support from the Australian Research Council Future Fellowship FT120100060 and Australian Research Council Discovery Projects DP120102576 and DP140104183. He acknowledges a fruitful discussion with Prof. Keilis-Borok in 1991 at the International Centre for Theoretical Physics, in Trieste, Italy, and also thanks the organizers of the Workshop on Non-Linear Dynamics and Earthquake Prediction that facilitated his participation by funding his visit and attendance to the event.

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    Pablo Moscato & Natalie Jane de Vries

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    Pablo Moscato

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    Natalie Jane de Vries

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Moscato, P., de Vries, N.J. (2019). Marketing Meets Data Science: Bridging the Gap. In: Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas. Springer, Cham. https://doi.org/10.1007/978-3-030-06222-4_1

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