Study information

# Statistical Modelling - 2023 entry

MODULE TITLE CREDIT VALUE Statistical Modelling 15 ECMM459 Dr Tinkle Chugh (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 7
 Number of Students Taking Module (anticipated) 90
DESCRIPTION - summary of the module content

*** This module is a “professional” module intended to be taught in a short-fat format based around 3-day teaching blocks, as part of the MSc Data Science (Professional) programme. ***

In this course we look at the concepts and methods of modern statistics in greater detail. The course will cover various topics in statistical modelling with Bayesian flavor, including generalised linear models, Hierarchical statistical models, Generative and Discriminative models, Hidden Markov models, use of Markov Chain Monte Carlo and Gaussian processes. The module will include practical application of these techniques as well as theoretical underpinnings and model choice.

Pre-requisites: ECMM456 Fundamentals of Data Science (Professional)

Co-requisites: None.

AIMS - intentions of the module

The aim of this module is to introduce you to modern methods in statistics, both conceptually and computationally.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Demonstrate a sound understanding of the reasoning behind choice of methods in statistical modelling.

2. Apply a range of statistical modelling techniques to real-life situations and datasets.

3.Perform data analyses by understanding the underlying principles behind different methods.

Discipline Specific Skills and Knowledge

4. Show sufficient knowledge of modern statistical methods both conceptual and computational.

Personal and Key Transferable / Employment Skills and Knowledge

5.  Reason using abstract ideas, formulate and solve problems and communicate reasoning and solutions effectively in writing.

6. Use learning resources appropriately.

7. Exhibit self management and time management skills.

SYLLABUS PLAN - summary of the structure and academic content of the module
Topics will include:

• Basics of Bayesian statistical modelling

• Generalised Linear Models

• Markov Chain Monte Carlo

• Generative and discriminative models

• Hierarchical statistical modelling

• Hidden Markov models

• Introduction to Gaussian Processes

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
 Scheduled Learning & Teaching Activities Guided Independent Study Placement / Study Abroad 34 46 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
 Category Hours of study time Description Scheduled learning and teaching activities 20 Lectures Scheduled learning and teaching activities 14 Workshop/Practical classes in a computer lab Guided independent study 46 Coursework preparation and self-study

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
 Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method Exercise/Quiz 1h x 4 All Written

SUMMATIVE ASSESSMENT (% of credit)
 Coursework Written Exams Practical Exams 100 0 0
DETAILS OF SUMMATIVE ASSESSMENT
 Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method Coursework report 100 2000-3000 words All Written

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
 Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment Coursework report Coursework report All Ref/Def period

RE-ASSESSMENT NOTES

Reassessment will be by coursework  in the failed or deferred element only. For referred candidates, the mark will be capped at 50%. For deferred candidates, the mark will be uncapped.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Type Author Title Edition Publisher Year ISBN
Set Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A. and Rubin, D. Bayesian data analysis 3rd CRC 2008
Set Gamerman, D. and Lopes H. F. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference CRC Press 2006
Set Banerjee, S., Bradley, P. Carlin, A.& Gelfand, E. Hierarchical Modeling and Analysis for Spatial Data CRC Press 2014
Set Donovan, Therese and Mickey, Ruth M. Bayesian Statistics for Beginners: a step-by-step approach OUP Oxford 2019 9780198841296
Set Carl Edward Rasmussen, Christopher K. I. Williams Gaussian Processes for Machine Learning MIT Press 2006 978-0262182539
Set Murphy, K. Machine Learning: A Probabilistic Perspective 1st MIT Press 2012 978-0-262-018029
CREDIT VALUE ECTS VALUE 15 7.5
PRE-REQUISITE MODULES ECMM456
NQF LEVEL (FHEQ) AVAILABLE AS DISTANCE LEARNING 7 No Monday 5th August 2019 Wednesday 18th January 2023
KEY WORDS SEARCH Statistical Modelling