CS7DS3 – Applied Statistical Modelling

Module CodeCS7DS3
Module NameApplied Statistical Modelling
ECTS Weighting [1]5 ECTS
Semester TaughtSemester 2
Module Coordinator/s  Dr Arthur White

Module Learning Outcomes

On successful completion of this module, students will be able to:

  1. Define a Markov chain and describe its theory;
  2. Identify an appropriate Monte Carlo simulation method for a given probability distribution and implement it;
  3. Describe and interpret complex statistical models in terms of a graphical model framework;
  4. Describe and implement the state of the art methodology in several topical applications in data science;
  5. Describe and discuss different approaches for model selection;
  6. Complete a data science project that applies the methods of this and other modules to a real data set.

Module Content

This module continues on from CS7CS4 (Machine Learning) with a focus on sampling methods and topical applications. It also gives an opportunity for students to apply, through a large project, the methods that they have explored in CS7DS1 (Data Mining & Analytics) and that they are currently exploring in CS7DS2 (Optimisation Algorithms for Data Analysis).

Teaching and Learning Methods

Mainly lectures, and some case studies and other supplemental reading will also be provided.

Assessment Details

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of TotalWeek SetWeek Due
Short Assignment Problem SetsLO1/LO215%Week 6Week 8
Main AssignmentDetailed report of a statistical analysis of a real data set. LO2, LO3/LO4, LO5, LO670%Week 8Week 11
Short AssignmentProblem Sets LO3/LO515%N/AN/A

Reassessment Details

Analysis of main assignment repeated and re-written, (100%).

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:22 hours
Lecture22 hours
Laboratory0 hours
Independent Study (outside scheduled contact hours), broken down by:94 hours
Preparation for classes and review of material (including preparation for examination, if applicable)42 hours
Completion of assessments (including examination, if applicable)52 hours
Total Hours116 hours

Recommended Reading List

  • P.D. Hoff, A first course in Bayesian statistical methods. Springer, 2009.
  • S.N. Wood, Core Statistics. Cambridge University Press, 2015.
  • C.M. Bishop, Pattern recognition and machine learning. Springer, 2006.
  • A. Gelman, J. B. Carlin, H.S. Stern, D.B. Rubin, Bayesian data analysis, 2nd edition. Chapman & Hall/CRC, 2004

Module Pre-requisites

Prerequisite modules: CS7DS1 (Data Mining & Analytics), CS7CS4 (Machine Learning).

Other/alternative non-module prerequisites: Knowledge of basic probability and statistical inference, and a familiarity with R will be beneficial. Some background knowledge of supervised and unsupervised machine learning methods are also assumed.

Module Co-requisites


Module Website


Arthur White SCSS