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Data Driven pp 87-127 | Cite as

Principles of Data Science: Advanced

  • Jeremy David Curuksu
Chapter
Part of the Management for Professionals book series (MANAGPROF)

Abstract

This chapter covers advanced analytics principles and applications. Let us first back up on our objectives and progress so far. In Chap.  6, we defined the key concepts underlying the mathematical science of data analysis. The discussion was structured in two categories: descriptive and inferential statistics. In the context of a data science project, these two categories may be referred to as unsupervised and supervised modeling respectively. These two categories are ubiquitous because the objective of a data science project is always (bear with me please) to better understand some data or else to predict something. Chapter  7 thus again follows this binary structure, although some topics (e.g. computer simulation, Sect. 7.3) may be used to collect and understand data, forecast events, or both.

References

  1. 165.
    Ng A (2008) Artificial intelligence and machine learning, online video lecture series. Stanford University, Stanford. www.see.stanford.edu Google Scholar
  2. 174.
    Löytynoja A (2014) Machine learning with Matlab, Nordic Matlab expo 2014. MathWorks, Stockholm. www.mathworks.com/videos/machine-learning-with-matlab-92623.html Google Scholar
  3. 177.
    Abdi H, Williams LJ (2010) Principal component analysis, Wiley interdisciplinary reviews. Comput Stat 2(4):433–459CrossRefGoogle Scholar
  4. 178.
    Dyke P (2001) An introduction to Laplace transforms and Fourier series. Springer, LondonCrossRefGoogle Scholar
  5. 179.
    Pereyra M, Ward L (2012) Harmonic analysis: from Fourier to wavelets, American Mathematical Society. Institute for Advanced Study, Salt Lake CityCrossRefGoogle Scholar
  6. 180.
    Aggarwal CC, Reddy CK (2013) Data clustering: algorithms and applications. Taylor and Francis Group, Boca RatonGoogle Scholar
  7. 181.
    Box G, Jenkins G (1970) Time series analysis: forecasting and control. Holden-Day, San FranciscoGoogle Scholar
  8. 182.
    Peter Ď, Silvia P (2012) ARIMA vs. ARIMAX - Which approach is better to analyze and forecast macroeconomic time series. In: Proceedings of 30th international conference mathematical methods in economics, pp 136–140Google Scholar
  9. 183.
    Chen R, Schulz R, Stephan S (2003) Multiplicative SARIMA models. In: Computer-aided introduction to econometrics. Springer, Berlin, pp 225–254CrossRefGoogle Scholar
  10. 184.
    Kuznetsov V (2016) Theory and algorithms for forecasting non-stationary time series, Doctoral dissertation, New York UniversityGoogle Scholar
  11. 185.
    Wilmott P (2007) Paul Wilmott introduces quantitative finance. Wiley, ChichesterGoogle Scholar
  12. 186.
    Hull JC (2006) Options, futures, and other derivatives. Pearson, Upper Saddle RiverGoogle Scholar
  13. 187.
    Torben A, Chung H, Sørensen B (1999) Efficient method of moments estimation of a stochastic volatility model: a Monte Carlo study. J Econ 91:61–87CrossRefGoogle Scholar
  14. 188.
    Rubinstein R, Marcus R (1985) Efficiency of multivariate control variates in Monte Carlo simulation. Oper Res 33:661–677CrossRefGoogle Scholar
  15. 189.
    Hammersley J, Morton K (1956) A new Monte Carlo technique: antithetic variates. In: Mathematical proceedings of the Cambridge philosophical society, vol 52, pp 449–475Google Scholar
  16. 190.
    Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151CrossRefGoogle Scholar
  17. 191.
    Hill TL (1956) Statistical mechanics: principles and selected applications. McGraw Hill, New YorkGoogle Scholar
  18. 192.
    Brin M, Stuck G (2002) Introduction to dynamical systems. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  19. 193.
    Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nature 9(9):646–652Google Scholar
  20. 194.
    Case D (1994) Normal mode analysis of protein dynamics. Curr Opin Struct Biol 4(2):285–290CrossRefGoogle Scholar
  21. 195.
    Alpaydin E (2014) Introduction to machine learning. MIT Press, BostonGoogle Scholar
  22. 196.
  23. 197.
    Harrell F (2013) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New YorkGoogle Scholar
  24. 198.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  25. 199.
    Owens J et al (2008) GPU computing. Proc IEEE 96:879–899CrossRefGoogle Scholar
  26. 200.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556Google Scholar
  27. 201.
    Cho K, Van Merriënboer B, et al (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv 1406.1078Google Scholar
  28. 202.
    Mnih V, Kavukcuoglu K et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533CrossRefGoogle Scholar
  29. 203.
    Kumar A, Irsoy O et al (2016) Ask me anything: dynamic memory networks for natural language processing. In: international conference on machine learning, pp 1378–1387Google Scholar
  30. 204.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  31. 205.
    Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRefGoogle Scholar
  32. 206.
    Brown C, Davis H (2006) Receiver operating characteristic curves and related decision measures: a tutorial. Chemom Intell Lab Syst 80:24–38CrossRefGoogle Scholar
  33. 207.
    Gero J, Udo K (2007) An ontological model of emergent design in software engineering, ICED’07 Google Scholar
  34. 208.
    Hundt M, Mollin S, Pfenninger S (2017) The changing English language: psycholinguistic perspectives. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  35. 209.
    Landauer T (2006) Latent semantic analysis. Wiley, New YorkCrossRefGoogle Scholar
  36. 210.
    Bird S (2006) NLTK: the natural language toolkit. In: Proceedings of the COLING/ACL on Interactive presentation sessions, pp 69–72, Association for Computational LinguisticsGoogle Scholar
  37. 211.
    See for example IBM Watson Alchemy Language API. https://www.ibm.com/watson/developercloud/alchemy-language.html
  38. 212.
    Google’s Software Beats Human Go Champion (2016) Wall Street Journal, Mar 9Google Scholar
  39. 213.
    Larousserie D, Tual M (2016) Première défaite d’un professionnel du go contre une intelligence artificielle, Le Monde, Jan 27Google Scholar
  40. 214.
    Parkinson’s Progression Markers Initiative – PPMI, Michael J. Fox foundation. www.ppmi-info.org

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jeremy David Curuksu
    • 1
  1. 1.Amazon Web Services, IncNew YorkUSA

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