The aim of this course is to learn and apply a range of Statistical Modelling and Analysis techniques applicable for data analysis
Course Description: Link to Official Course Descriptor.
Location: Dubai, Edinburgh.
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 and critical understanding of the concepts, issues, principles and theories of statistical modelling and analysis
- Critical theoretical and detailed practical knowledge of statistical modelling and analysis techniques
- Practical professional experience of analysing, designing, implementing and validating experiments using common statistical techniques
Learning Outcomes: Personal Abilities
- Ability to deal with complex issues and make informed professional judgements about statistical models and analysis
- Exercise substantial autonomy and initiative in performing data analysis.
- Showing initiative and good professional team working skills in shared data analysis. (PDP)
- Demonstrate critical reflection on statistical modelling and analysis issues. (PDP)
Assessment Methods: Due to covid, assessment methods for Academic Year 2021/22 may vary from those noted on the official course descriptor. Please see:
- Maths (F1) Course Weightings 2021/22
- Computer Science (F2) Course Weightings 2021/22
- AMS (F7) Course Weightings 2021/22
SCQF Level: 11.