|Module Name||Data Analytics|
|ECTS Weighting||10 ECTS|
|Semester taught||Semester 1|
|Module Coordinator/s||Bahman Honari|
Module Learning Outcomes
On successful completion of this module, students will be able to:
- To understand the theory and apply the following techniques to a set of data.
- L01. Classification and Regression trees
- L02. Ensemble methods inlcuding, Bagging, Random forests, Boosting including Gradient Boosting and Extreme Gradient Boosting.
- L03. To evaluate all of the above modules.
The aim of the course is to introduce the students to a set of techniques including classification and regression trees, and ensemble methods. Methods to evaluate models will also be discussed. The following topics will be addressed:
Overview of Data Analytics, Handling data, Missing data, Derived Variables, Detailed discussion of Detailed discussionof Evaluating models Handling unbalanced datasets Stacking.
Teaching and learning Methods
4 lectures and 1 lab per week.
|Assessment Component||Brief Description||Learning Outcomes Addressed||% of total||Week set||Week Due|
|Assignment||Assignment Application of techniques to a data-set||L01, L02, L03||30||Week 8 of term||Revision week|
|Exam||Real-time Exam (3 hours)||L01, L02, L03||70||Exam week|
Same as above (Assignment (30%) and Final Exam (70%))Individual assignment 100%
Contact Hours and Indicative Student Workload
|Contact Hours (scheduled hours per student over full module), broken down by:||55 hours|
|Tutorial or seminar||22 hours|
|Independent study (outside scheduled contact hours), broken down by:||126 hours|
|Preparation for classes and review of material (including preparation for examination, if applicable||66 hours|
|completion of assessments (including examination, if applicable)||60 hours|
|Total Hours||181 hours|
Recommended Reading List
Hastie Trevor, Tibshirani, R., Friedman, J. The Elements of Statistical Learning, 2nd Edition, Springer Series, 2009
Kuhn, Max & Johnson, K. Applied Predictive Modeling, Springer, 2013
Seni, G. and Elder J. Ensemble methods in Data Mining, Morgan & Claypool, 2010 Detailed list will be handed out.
Prerequisite modules: NA
Other/alternative non-module prerequisites: NA