World Business StrategiesServing the Global Financial Community since 2000

Reading List

Data Analysis textbooks:

  • The Data Science Design Manual [13] — a recent text focussing on the practicalities of data analysis and data science.
  • Intelligent Data Analysis edited by Michael Berthold and David J. Hand [1] — contains contributed chapters introducing many key ideas of data analysis, but mostly machine learning.
  • Statistics and Finance: An Introduction by David Ruppert [10] — a concise but thorough introductory text on statistics, data analysis, and their applications to finance.
  • Statistics and Data Analysis for Financial Engineering [11] — a deeper and broader, graduate-level version of the above.

 Machine Learning textbooks (general):

  • Intelligent Data Analysis edited by Michael Berthold and David J. Hand [1] — contains contributed chapters introducing many key ideas of data analysis, but mostly machine learning.
  • An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani [7]— an introductory-level textbook, avaialable online: http://www-bcf.usc.edu/~gareth/ISL/
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman [5] — a deeper and broader text by some of the same authors. Very popular in the electronic machine learning community.
  • Data Analysis in Q for Quants and Data Scientists [2] by Paul Bilokon and Jan Novotny is still in preparation and is due to appear next year.

Machine Learning textbooks (specialised)

Further reading

  • Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville [4]—a recent text focussing on neural networks, especially deep neural networks. Few other texts go into this subject as far. Also contains good introductory chapters on basic machine learning. Available online for free: http://www.deeplearningbook.org/
  • An Introduction to Support Vector Machines and other kernel-based learning methods [3] — a brief, introductory text on support vector machines.
  • Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond by Bernhard Sch¨olkopf and Alexander J. Smola [12]— a deeper text on support vector machines.
  • Optimization for Machine Learning by Suvrit Sra, Sebastian Nowozin, and Stephen J.Wright [14]—contributed chapters focussing on deeper aspects of applications of optimisation in machine learning.
  • Gaussian Processes for Machine Learning [9] — as the title suggests, this succinct text focusses on Gaussian processes.

 Python textbooks 

  • Python for Finance: Analyze Big Financial Data by Yves Hilpisch [6].
  • Python for Data Analysis: Data Wrangling, NumPy and IPython by Wes McKinney [8].

 

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