MAS8403 : Statistical Foundations of Data Science

• Offered for Year: 2024/25
• Module Leader(s): Dr Clement Lee
• Owning School: Mathematics, Statistics and Physics
• Teaching Location: Newcastle City Campus
Semesters

Your programme is made up of credits, the total differs on programme to programme.

 Semester 1 Credit Value: 10 ECTS Credits: 5.0 European Credit Transfer System

Aims

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
-       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 ActivitiesLecture62:0012:00Present in person lectures
Guided Independent StudyAssessment preparation and completion112:0012:00Formative exercise
Scheduled Learning And Teaching ActivitiesPractical62:0012:00Present in Person structured synchronous practical
Guided Independent StudyProject work148:0048:00Main project
Scheduled Learning And Teaching ActivitiesDrop-in/surgery41:004:00Present in person drop-in
Guided Independent StudyIndependent study62:0012:00Lecture follow-up/background reading
Total100:00
Teaching Rationale And Relationship

Lectures and set reading are used for the delivery of theory and explanation of methods, illustrated with examples. Practicals are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied. There are two present-in-person practical sessions per week to ensure rapid feedback on understanding. Scheduled present-in-person drop-ins provide opportunity for students to ask questions and receive immediate feedback.

Assessment Methods

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

Other Assessment
Description Semester When Set Percentage Comment
Report1M100Main module project (8 pages)
Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Description Semester When Set Comment
Practical/lab report1MA compulsory report allowing students to develop problem solving techniques, to practise the methods learnt, and to assess progress
Assessment Rationale And Relationship

A compulsory formative practical report allows the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback, before the summative assessment.

In a foundational subject like the Mathematical Sciences, there is research evidence to suggest that continual consolidation of learning is essential and the fewer pieces of assessment there are, the more difficult it is to facilitate this. On this module, it is particularly important that the material on the earlier formative assessment is fully consolidated, before the later assessment is attempted.