BUiD Library: INF 610 Big Data and Analytics: Reading list

INF 610 Big Data and Analytics

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

Acerbi, A., Lampos, V., Garnett, P. and Bentley, R.A. (2013). The expression of emotions in 20th century books. PLoS ONE, vol. 8(3), e59030.
Ahn, Y., Ahnert, S.E., Bagrow, J. P. and Barabási, A. (2011). Flavor network and the principles of food pairing. Scientific Reports 1, Article number: 196. Open resource
Anderson, C. (2008). The end of theory: the data deluge makes the scientific method obsolete. Wired magazine.
Dave, M. and Gianey, H. (2016). Different clustering algorithms for big data analytics: a review. In Proceedings of the International Conference on System Modeling & Advancement in Research Trends (SMART), (pp. 328-333). IEEE. Request this item
Dean, J. and Ghemawat, S. (2010). MapReduce: a flexible data processing tool. Communications of the ACM, vol. 53(1), pp. 72-77.
Domingos, P. (2012). A few useful things to know about machine learning. CACM, vol. 55(10).
Heer, J., Bostock, M. and Ogievetsky, D. (2010). A tour through the visualization zoo. Communications of the ACM, vol. 53(6).
Landset, S., Khoshgoftaar, T.M., Richter, A.N. and Hasanin, T. (2015). A survey of open source tools for machine learning with big data in the Hadoop ecosystem. Journal of Big Data, vol.2(1), p.24.
Lazer, D., Kennedy, R., King, G. and Vespignani, A. (2014). The parable of Google flu trends: traps in big data analysis. Science, vol. 343(6176), pp. 1203-1205. Request this item
L'Heureux, A., Grolinger, K., ElYamany, H. F. and Capretz, M. (2017). Machine learning with big data: challenges and approaches. IEEE Access. Open access
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y. and Alsaadi, F.E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, vol. 234, pp.11-26.
Lohr, S. (2014). Google flu trends: the limits of big data. NYTimes, March 28. Open access
Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R. and Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, vol.2(1), p.1.
Salloum, S., Dautov, R., Chen, X., Peng, P.X. and Huang, J.Z. (2016). Big data analytics on Apache Spark. International Journal of Data Science and Analytics, pp.1-20. Open access
Sapountzi, A. and Psannis, K.E. (2016). Social networking data analysis tools & challenges. Future Generation Computer Systems.
Stonebraker, M. (2010). MapReduce and parallel DBMS’s: friends or foes? Communications of the ACM, vol. 53(1), pp. 64-71. Open access
Wu, X. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, vol. 14.
Benford's Law (wikipedia)
Frequentist vs. Bayesian Perspectives explained, Jake Vanderplas, UW eScience Institute

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