STU44003 – Data Analytics

Module CodeSTU44003
Module Name Data Analytics
ECTS Weighting[1]10 ECTS
Semester taughtSemester 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.

Module Content

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 Details

Assessment ComponentBrief Description Learning Outcomes Addressed% of totalWeek setWeek Due
Assignment Assignment Application of techniques to a data-setL01, L02, L0330 Week 8 of termRevision week
ExamReal-time Exam (3 hours) L01, L02, L0370Exam week

Reassessment Details

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
Lecture44
hours
Laboratory11 hours
Tutorial or seminar22 hours
Other0 hours
Independent study (outside scheduled contact hours), broken down by:126 hours
Preparation for classes and review of material (including preparation for examination, if applicable66 hours
completion of assessments (including examination, if applicable)60 hours
Total Hours181 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.  

Module Pre-requisites

Prerequisite modules: NA

Other/alternative non-module prerequisites: NA

Module Co-requisites

None

Module Website

Blackboard