Computer Science

COMM418DA - Statistical Modelling (2023)

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MODULE TITLEStatistical Modelling CREDIT VALUE15
MODULE CODECOMM418DA MODULE CONVENERDr Tinkle Chugh (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 90
DESCRIPTION - summary of the module content
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: COMM415DA Fundamentals of Data Science (Professional)
Co-requisites: None.
 
This module is a part of the dual-qualification MSc Data Science (Professional) / Level 7 Research Scientist Apprenticeship programme. It cannot be taken as an elective by students on other programmes. After successful completion of the programme, students will graduate with MSc Data Science and (subject to additional completion of the End Point Assessment) the Level 7 Research Scientist Apprenticeship. 
 
The apprenticeship standard and other documentation relating to the Level 7 Research Scientist Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/research-scientist-v1-0.
AIMS - intentions of the module
The aim of this module is to introduce you to modern methods in statistics, both conceptually and computationally.
 
In addition to its academic aims as part of the programme, this module has specific aims as part of the Level 7 Research Scientist Apprenticeship. The full list of Knowledge, Skills and Behaviours that must be demonstrated to complete the Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/research-scientist-v1-0.
 
This module will deliver content that may be used to evidence the Knowledge, Skills and Behaviours set out below. Primarily: K1 and K5. Secondarily: S1, S2 and S6.
 
Knowledge (K), Skill (S) or Behaviour (B)
K1: Subject specific knowledge: A deep and systemic understanding of a named / recognised scientific subject as found in an industrial setting, such as biology, chemistry or physics, found in the nuclear, food manufacture, pharmacology or energy production sectors, at a level that allows strategic and scientific decision making, while taking account of inter relationships with other relevant business areas / disciplines.
K5: Data analysis and evaluation: Statistical analysis techniques, numerical modelling techniques and how they are applied in context. How to interpret and categorise data to make informed and objective decisions against the goals and targets of the project. How to evaluate and interpret the data and associated analysis against company objectives.
S1: Scientific Knowledge: Apply a range of advanced, new and emerging practical and experimental skills appropriate to the role (e.g. chemical synthesis, bio analysis, computational modeling). 
S2: Data Collection and Reporting: Capture and evaluate data critically drawing a logical conclusion, e.g. Case Report Forms, Data Management Plans, Data Review Plans, edit checks and User Acceptance Testing Plans.
S6: Critical Thinking: Conceptualise, evaluate and analyse information to solve problems.
 
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 40.00 Guided Independent Study 110.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category

Hours of study time

Description

Scheduled learning and teaching activities

40

Lectures/Workshop/Practical classes

Guided independent study

110

 Coursework preparation, reading 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 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment

% of Credit

Size of Assessment (e.g. duration/length)

ILOs Assessed

Feedback Method

Coursework

60

(e.g.) 2000-3000 words

All

Written
Coursework 40 (e.g.) Class test of 2 hours All Written/ELE

 

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

Within 8 weeks

 

RE-ASSESSMENT NOTES

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

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

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A. and Rubin, D. Bayesian data analysis 3rd CRC 2008 [Library]
Set Gamerman, D. and Lopes H. F. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference CRC Press 2006 [Library]
Set Banerjee, S., Bradley, P. Carlin, A.& Gelfand, E. Hierarchical Modeling and Analysis for Spatial Data CRC Press 2014 [Library]
Set Donovan, Therese and Mickey, Ruth M. Bayesian Statistics for Beginners: a step-by-step approach OUP Oxford 2019 9780198841296 [Library]
Set Carl Edward Rasmussen, Christopher K. I. Williams Gaussian Processes for Machine Learning MIT Press 2006 978-0262182539 [Library]
Set Murphy, K. Machine Learning: A Probabilistic Perspective 1st MIT Press 2012 978-0-262-018029 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES COMM415DA
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 05 August 2019 LAST REVISION DATE Tuesday 24 January 2023
KEY WORDS SEARCH Statistical Modelling