Page 137 - Handbook Bachelor Degree of Science Academic Session 20202021
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Faculty of Science Handbook, Academic Session 2020/2021


               Assessment:                                     References:
               Continuous Assessment:       40%                1.   Adriaans,  P.,  &  Zantige,  D.  (1996).  Data  mining.
               Final Examination:           60%                    Addison-Wesley.
                                                               2.    Hand, D., Mannila, H., & Smyth, P. (2001). Principles of
               Medium of Instruction:                              data mining. MIT Press.
               English                                         3.    Cios.  K.  J.  et  al.  (2010).  Data  mining:  A  knowledge
                                                                   discovery approach. New York: Springer-Verlag
               Soft Skills:
               CS3, CTPS3
                                                               SIT3011      BIOINFORMATICS
               References:
               1.   Scheaffer,  R.  L.  (2006),  Elementary  survey  sampling   Statistical  modelling  of  DNA/protein  sequences:
                    th
                   (6  ed.). Duxbury.                          Assessing  statistical  significance  in  BLAST  using  the
                                             nd
               2.   Thompson, S. K. (2002), Sampling (2  ed.). Wiley.   Gumbel distribution; DNA substitution models; Poisson and
               3.   Lohr, Sharon L. (2010). Sampling: Design and analysis   negative binomial models for gene counts; Hidden Markov
                   (2  ed). Cengage Learning.                  Model.
                    nd
                                                     rd
               4.   Cochran,  W.  (1977).  Sampling  techniques  (3   ed.).
                   Wiley.                                      Algorithms for sequence analysis and tree construction:
                                                               Dynamic programming for sequence alignment and Viterbi
                                                               decoding;  neighbour-joining,  UPGMA,  parsimony  and
               SIT3009      STATISTICAL PROCESS CONTROL        maximum likelihood tree-building methods.

               Methods  and  philosophy  of  statistical  process  control.   Analysis  of  high-dimensional  microarray  /  RNA-Seq
               Control  charts  for  variables  and  attributes.  CUSUM  and   gene  expression  data:  Statistical  tests  for  detecting
               EWMA  charts.  Process  capability  analysis.  Multivariate   differential expression, feature selection, visualization, and
               control  charts.  Acceptance  sampling  by  attributes  and   phenotype classification.
               variables.
                                                               Assessment:
               Assessment:                                     Continuous Assessment:       40%
               Continuous Assessment:       40%                Final Examination:           60%
               Final Examination:           60%
                                                               Medium of Instruction:
               Medium of Instruction:                          English
               English
                                                               Soft Skills:
               Soft Skills:                                    CS3, CTPS3
               CS3, CTPS3
                                                               References:
               References:
               1.   D.  C.  Montgomery.  (2009).  Introduction  to  statistical   1.   Jones, N.C., & Pevzner, P.A. (2004). An introduction to
                   quality control (6  ed.). Wiley.                bioinformatics algorithms. Massachusetts: MIT Press.
                               th
               2.   R.  S.  Kenett,  &  S.  Zacks.  (1998).  Modern  industrial   2.    Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998).
                   statistics: Design and control of quality and reliability.   Biological  sequence  analysis:  Probabilistic  models  of
                   Duxbury Press.                                  proteins  and  nucleic  acids.  Cambridge:  Cambridge
               3.    A.  J.  Duncan.  (1986).  Quality  control  and  industrial   University Press.
                   statistics (5  ed.). Irwin.                 3.    Ewens, W.J., & Grant, G.R. (2005). Statistical methods
                           th
                                                                                             nd
                                                                   in bioinformatics: An introduction (2  ed.). New York:
                                                                   Springer.
               SIT3010      INTRODUCTION TO DATA MINING        4.    Pevsner,  J.  (2009).  Bioinformatics  and  functional
                                                                            nd
                                                                   genomics (2  ed.). New York: Wiley-Blackwell.
               Description: Introduction to statistical methods and tools for
               analysis of very large data sets and discovery of interesting
               and unexpected relationships in the data.       SIT3012   DESIGN AND ANALYSIS OF EXPERIMENTS

               Data preprocessing and exploration: data quality and data   Philosophy  related  to  statistical  designed  experiments.
               cleaning.  Data  exploration:  summarizing  and  visualizing   Analysis  of  variance.  Experiments  with  Blocking  factors.
               data;  principal  component,  multidimensional  scaling.  Data   Factorial experiments. Two level factorial designs. Blocking
               analysis  and  uncertainty:  handling  uncertainty;  statistical   and confounding system for two-level factorials. Two-level
               inference; sampling.                            fractional factorial designs.

               Statistical  approach  to  data  mining  and  data  mining
               algorithms:  Regression,  Validation;  classification  and   Assessment:
               clustering: k-means, CART, decision trees; Artificial Neural   Continuous Assessment:      40%
               Network;  boosting;  support  vector  machine;  association   Final Examination:        60%
               rules  mining.  Modelling:  descriptive  and  predictive
               modelling. Data organization.                   Medium of Instruction:
                                                               English
               Assessment:
               Continuous Assessment:       40%                Soft Skills:
               Final Examination:           60%                CS3, CTPS4
               Medium of Instruction:
               English                                         References:
                                                               1.   Montgomery,  D.C.  (2004).  Design  and  analysis  of
                                                                              th
               Soft Skills:                                        experiments (6  ed.). John Wiley.
               CS3, CTPS3

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