Module Descriptor School of Computer Science and Statistics
Module Code | CS7DS1 |
Module Name | DATA ANALYTICS |
Module Short Title | |
ECTS | 10 |
Semester Taught | 1st |
Contact Hours | 4 lectures and 1 lab per week. |
Module Personnel | Dr Bahman Honari |
Learning Outcomes | To understand the theory and be able to apply the following techniques to a set of data
Bagging Boosting Random forests RuleFit Procedute
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Learning Aims | 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. |
Module Content |
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Recommended Reading List |
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Module Prerequisites | A course on Multivariate Analysis covering principal components multiple regression, clustering techniques and logistic regression. A good working knowledge of R is also required. |
Assessment Details | Students will be required to carry out a project worth 40% of the total marks with an exam in the n accounting for the remaining 60%. Assessment in the Supplemental session will be based on 100% exam. |
Module Website | |
Academic Year of Data | 2018/19 |