SHE Level 4 SCQF Credit Points 20.00 ECTS Credit Points 10.00 Module Code MHI226699 Module Leader Gordon Morison School School of Computing, Engineering and Built Environment Subject Computing Trimester A (September start)

### Summary of Content

This module will cover advanced methods and techniques of Data Science. Students will learn how to extract useful information from large datasets using a variety of analytical techniques. Focus will be given to approaches such as Bayesian statistical analysis, specifically; prior and posterior distribution construction, decision theory and model selection. Students will also be introduced to Markov chain Monte Carlo methods and how they can be used to implement Bayesian models. Ultimately, students will be able to utilise Bayesian statistical techniques to build complex models of data.

### Syllabus

Bayesian Theory - Subjective Probability - Bayesian Inference - Prior Distributions - Posterior Distributions - Normal Distribution Analysis - Binomial Distribution Analysis - Exponential Distribution Analysis - Poisson Distribution Analysis - Predictive Inference - Decision Theory - Point Estimation - Hypothesis Testing - Bayesian Modelling - Hierarchical Models - Bayesian Computation - Markov Chain Monte Carlo Methods - Gibbs Sampling - Graphical Models - Bayesian/MCMC Implementation

### Learning Outcomes

On successful completion of the module. student should be able to:1. Prove and utilise Bayes theorem2. Critically analyse data from various distributions (normal, binomial, exponential, poisson)3. Understand the Bayesian approach to data analysis4. Utilise Bayesian analysis to develop solutions to statistical problems5. Understand and explain the Markov chain Monte Carlo (MCMC) approach to Bayesian implementation6. Understand and explain Statistical Decision Theory7. Implement Bayesian models using MCMC and appropriate software tools

### Teaching / Learning Strategy

Lectures are supplemented by directed reading to relevant sources both hard and electronic format and varied further reading is encouraged. Hands on experience is gained in the process of planning simulated projects. Students are supported in their studies by both face-to-face and on-line tutorials and online quiz material. Student assessment will be based around on-line techniques for three of the assessments and a class based test for the remaining assessment. Learning and teaching strategies will be developed and implemented, appropriate to students' needs, to enable all students to participate fully in the module.

Bayesian Statistics: An Introduction, 4th Edition: An Introduction, 4th Edition Paperback - 4 Sep 2012 by Peter M. Lee Bayesian Data Analysis, Third Edition - 6 Dec 2013 by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin Data Analysis: A Bayesian Tutorial Paperback - 27 Jul 2006 by Devinderjit Sivia

### Transferrable Skills

Critical thinking and problem solving (D1) Time management (organising and planning work) (D6) Information retrieval skills (D10) IT Skills (D13) Communication skills, written, oral and listening (D14)

### Module Structure

Activity Total Hours
Assessment (FT) 28.00
Tutorials (FT) 24.00
Independent Learning (FT) 112.00
Seminars (FT) 12.00
Lectures (FT) 24.00

### Assessment Methods

Component Duration Weighting Threshold Description
Course Work 02 n/a 50.00 35% Programming assignment
Course Work 01 n/a 50.00 35% Programming assignment