Module Title:   Statistical Data Analysis

Module Credit:   20

Module Code:   CM-0428D

Academic Year:   2015/6

Teaching Period:   Semester 2

Module Occurrence:   A

Module Level:   FHEQ Level 7

Module Type:   Standard module

Provider:   Computer Science

Related Department/Subject Area:   SCIM (Dept of Computer Science)

Principal Co-ordinator:   Dr Y Peng

Additional Tutor(s):   Dr A Konstadopoulou

Prerequisite(s):   None

Corequisite(s):   None

To acquire knowledge of statistical data analysis, statistical learning methods and data analytics techniques for the hypotheses generation and hypotheses testing, in order for making appropriate statements/predictions.

Learning Teaching & Assessment Strategy:
A number of lectures, laboratory practices will provide essential theoretical knowledge, and practical skill. Practical examples will be provided to aid the learning. Discussion groups during laboratory sessions on each of the topics will enable critical engagement and analysis and application of the knowledge using the software package of R.

Lectures:   24.00          Directed Study:   150.00           
Seminars/Tutorials:   0.00          Other:   0.00           
Laboratory/Practical:   24.00          Formal Exams:   2.00          Total:   200.00

On successful completion of this module you will be able to...

(a) Critically assess the scientific as well as the practical value of data;
(b) Apply statistical hypothesis testing, statistical learning, and statistical modelling appropriately for the analysis of data;
(c) Gain practical understanding and knowledge of how established procedures are used to make inferences in a disciplined manner.

On successful completion of this module you will be able to...

(a) Apply the procedures from data collection, modelling and turn it into information;
(b) Identify relationships (model generation, using statistical learning techniques) between different data variable;
(c) Provide appropriate models for predictive analysis.

On successful completion of this module you will be able to...

(a) To gain confidence handling quantitative and qualitative information within a random and uncertain environment;
(b) Write technical reports as appropriate; work in groups to plan complex work and achieve organisational objectives; use appropriate sources to search for, retrieve, and organise information; [QCA level 3];
(c) Identify relevant sources of information; undertake search evaluation and selection of information. Identify appropriate methods of achieving high quality outcomes [QCA level 4 IT].

  Coursework   50%
  A report discussing the analysis of given dataset
  Examination - closed book 2.00 50%
  Examination - closed book 2 hours
  Examination - closed book 2.00 100%
  Supplementary Examination - closed book 2 hours

Outline Syllabus:
This module covers the techniques and methods concerning the data analytics, model generation and prediction for a wide range of applications:
(1) Statistical method for data characteristics;
(2) Model generation: applying statistical method for data analytics including (a) hypothesis generation, e.g. linear models, nearest neighbour methods, function approximation, and (b) statistical hypothesis testing such as parametric and nonparametric methods; bivariate techniques including contingency tables, correlation and regression; multivariate procedures including multiple regression, logistic regression and discrimination techniques;
(3) Decision and prediction: applying statistical learning and data-driven modelling approach to infer new knowledge and probabilistic model from the models generated, including Bayesian decision theory, classification and clustering.

Version No:  2