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INF 610 Big Data and Analytics: Reading list


Department:  PhD in Computer Science 
Module Description: This module provides students with an opportunity to gain an in depth understanding of the theories and issues on analytics and big data. The course will cover how big data is collected, stored, and analysed. Students will also learn about the main challenges faced when dealing with big data. Practical case studies will be used for illustration.

 

Module text(s)

  • There is no textbook. 

 

Recommended readings

             http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/

             http://jakevdp.github.io/blog/2014/06/06/frequentism-and-bayesianism-2-when-results-differ/

             http://jakevdp.github.io/blog/2014/06/12/frequentism-and-bayesianism-3-confidence-credibility/

             http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/

  • Hans Rosling, The Joy of Stats
  • Pat Hanaran, Tools for Data Enthusiasts

 

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