BIA656 Statistical Learning and Analytics
Business Intelligence & Analytics
Wed. 6:15-8:45 PM
Tue. 9:30-11:00 AM
  • Fall On Campus
  • Spring On Campus

The significant amount of corporate information available requires a systematic and analytical approach to select the most important information and anticipate major events. Statistical learning algorithms facilitate this process understanding, modeling and forecasting the behavior of major corporate variables.

This course introduces time series and statistical and graphical models used for inference and prediction. The emphasis of the course is in the learning capability of the algorithms and their application to several business areas.

Prerequisites: Basic course in probability and statistics at the level of MGT 620 or BIA 654 Multivariate data analytics.


Students will:
• Learn the fundamental concepts of time series analysis and statistical learning algorithms.
• Explore existent and new applications of time series and statistical learning methods to business problems, and to generic classification problems.

Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer-Verlag, New York,. 2010 (downloadable at http://www-stat.stanford.edu/~tibs/ElemStatLearn/).

Analysis of Financial Time Series, Ruey S. Tsay, 3rd Ed, John Wiley, 2010. (2nd. Edition can be accessed through the library website. Only chapter 2) 


Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze,Cambridge University Press. 2008 (downloadable at http://nlp.stanford.edu/IR-book). 


Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2006.
Pattern Classification, R.O. Duda, P.E. Hart and D.G. Stork, John Wiley & Sons, 2001.
Machine Learning, McGraw-Hill Series in Computer Science,Tom M. Mitchell, 1997.
Seven methods for transforming corporate data into business intelligence. Upper Saddle River: Prentice Hall. 1997. Vasant Dhar and Roger Stein.



R and Weka (http://www.cs.waikato.ac.nz/ml/weka) are the main software packages that will be used. No prior knowledge of Weka is required.