Page 47 - Handbook PG 20182019

Faculty of Science Postgraduate Booklet, Session 2018/2019

Data reduction: Data organisation and display; Principal component, multidimensional scaling.

Data Analysis and uncertainty: Handling uncertainty; statistical inference; sampling

Data mining Algorithms: Classification and clustering – CART; artificial neural network; support
vector machine; mining ordered dependence.

Modelling: Model Structure; curse of dimensionality; score function; optimisation methods;
descriptive modelling and prediction. Data organisation.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Computer programming

Humanity Skill:
TS5, LL3, LS3

References:
1. Cios, K.J., Pedrycz, W., Swiniarski, R.W. and Kurgan, L.A. (2007). Data Mining: A Knowledge
Discovery Approach. Springer, New York, USA.
2. Kamath, C. (2009). Scientific Data Mining: A Practical Perspective. Society for Industrial and
Applied Mathematics, Philadelphia, USA.
3. Bramer, M. (2013). Principles of Data Mining. 2nd Ed., Springer-Verlag, New York.

SQB7020 Survival Data Analysis

Basic concepts such as survival and hazard functions. Survival data analysis including life table,
Kaplan-Maier; log-rank and Wilcoxon tests. Survival regression modelling including the Cox
regression model, several parametric models and the accelerated life time model and risk model.
Diagnostic checking of the models. Application to the real dataset.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English
Transferable Skills:
Skills in analyzing medical data sets

Humanity Skill:
CS6, CT5, EM3

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