Statistical Modelling - 2025 entry
MODULE TITLE | Statistical Modelling | CREDIT VALUE | 15 |
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MODULE CODE | ECMM459 | MODULE CONVENER | Dr Tinkle Chugh (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 7 |
Number of Students Taking Module (anticipated) | 90 |
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*** 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.
The aim of this module is to introduce you to modern methods in statistics, both conceptually and computationally.
Scheduled Learning & Teaching Activities | 34 | Guided Independent Study | 46 | Placement / Study Abroad | 0 |
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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 |
|
Form of Assessment |
Size of Assessment (e.g. duration/length) |
ILOs Assessed |
Feedback Method |
Exercise/Quiz |
1h x 4 |
All |
Written |
Coursework | 100 | Written Exams | 0 | Practical Exams | 0 |
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Form of Assessment |
% of Credit |
Size of Assessment (e.g. duration/length) |
ILOs Assessed |
Feedback Method |
Coursework report |
100 |
2000-3000 words |
All |
Written |
Original Form of Assessment |
Form of Re-assessment |
ILOs Re-assessed |
Time Scale for Re-assessment |
Coursework report |
Coursework report |
All |
Ref/Def period |
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.
information that you are expected to consult. Further guidance will be provided by the Module Convener
Reading list for this module:
Type | Author | Title | Edition | Publisher | Year | ISBN |
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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 | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | ECMM456 |
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CO-REQUISITE MODULES |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Monday 5th August 2019 | LAST REVISION DATE | Friday 28th February 2025 |
KEY WORDS SEARCH | Statistical Modelling |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.