F20SA Statistical Modelling and Analysis

Dr Mateusz MajkaAdrian Turcanu

Course co-ordinator(s): Dr Mateusz Majka (Edinburgh), Adrian Turcanu (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.

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:
- Maths (F1) Course Weightings 2021/22
- Computer Science (F2) Course Weightings 2021/22
- AMS (F7) Course Weightings 2021/22

SCQF Level: 10.

Credits: 15.