Course co-ordinator(s): Prof Damian Clancy (Edinburgh), Adrian Turcanu (Dubai), Dr Haslifah Hasim (Dubai).
Aims:
The aim of this course is to learn and apply a range of Statistical Modelling and Analysis techniques applicable for data analysis.
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: F17SC Discrete Mathematics.
Location: Dubai, Edinburgh, Malaysia.
Semester: 1.
Syllabus:
A practical understanding of:
- Basic probability concepts: Random variables and their distributions; how distributions relate to sampling scenarios.
- Joint distributions, Sums of random variables, Central limit theorems
- Classical inference: Point estimation, moment estimators and maximum likelihood; Confidence intervals – calculation and interpretation; Hypothesis testing and p-values
- Essentials of Bayesian inference: Priors and posteriors; Credible intervals; Predictive distributions
- Modelling approaches: Regression and ANOVA;
- Multivariate exploratory techniques: Principal Components Analysis + Factor Analysis; Introduction to non-parametric methods
- Practical elements in R or Python
Learning Outcomes: Subject Mastery
- Detailed understanding of the concepts, issues, principles and theories of statistical modelling and analysis
- Understanding and practical knowledge of statistical modelling and analysis techniques to apply suitable methodologies for a given task
- Practical experience of analysing, designing, implementing and validating experiments using common statistical techniques.
Learning Outcomes: Personal Abilities
Ability to deal with and make informed judgements about statistical models and analysis
Assessment Methods: Due to covid, assessment methods for Academic Year 2021-22 may vary from those noted on the official course descriptor. Please see the Computer Science Course Weightings and the Maths Course Weightings for 2020-21 Semester 1 assessment methods.
SCQF Level: 10.
Credits: 15.