MAS8403 : Statistical Foundations of Data Science

Semester 1 Credit Value: 10
ECTS Credits: 5.0


Statistics is a fundamental discipline in Data Science. This module aims to introduce the fundamental statistical and mathematical concepts and techniques underpinning modern computational statistics and data analysis. Furthermore, this module aims to provide students with the basic skills needed for statistical modelling, data analysis and computing that ground these statistics concepts in data science practice.

Outline Of Syllabus

-       Introduction to probability including axioms, basic probability rules, conditional probability and Bayes' Theorem
-       Random variables
-       Probability distributions
-       Populations and samples
-       Sampling methods, including issues of bias and representativeness
-       Collection of data; observational studies and designed experiments
-       Graphical and numerical summaries
-       Frequentist inference and repeated sampling
-       Maximum likelihood estimation
-       Basic constructs of R programming and R packages
-       R for data analysis and visualisation

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture92:0018:00Lectures
Guided Independent StudyAssessment preparation and completion10:300:30Oral Examination
Guided Independent StudyAssessment preparation and completion50:302:30Preparation for Oral Examination
Guided Independent StudyAssessment preparation and completion151:0015:00Coursework exercises
Guided Independent StudyDirected research and reading101:0010:00Background reading
Scheduled Learning And Teaching ActivitiesPractical92:0018:00Practical sessions
Guided Independent StudyProject work181:0018:00Project
Guided Independent StudyIndependent study92:0018:00Lecture follow-up
Teaching Rationale And Relationship

Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Practicals are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied. A practical is associated with each lecture in order to provide sufficient hands-on training and rapid feedback on understanding.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report1M45Up to 3 practical reports Word count: Up to 1,000 words as specified for each report.
Report1M55Project report Word count: Up to 1,500 words
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral PresentationMA structured discussion including a software demonstration and reflection on the key learning objectives of the coursework project.
Assessment Rationale And Relationship

Written assignments (approximately 3 pieces of work of approximately equal weight) followed by a larger piece of project work allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; the smaller pieces of work are thus formative as well as summative assessment.

The semi-structured interview facilitates a reflective discussion about how individual students have met the learning objectives of the module and how the principles of fundamental statistics are embedded in the functionality of their project work.

Reading Lists