Postgraduate

MAS8403 : Statistical Foundations of Data Science

• Offered for Year: 2021/22
• Module Leader(s): Dr Joe Matthews
• Owning School: Mathematics, Statistics and Physics
• Teaching Location: Newcastle City Campus
Semesters
 Semester 1 Credit Value: 10 ECTS Credits: 5.0

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
-       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
Guided Independent StudyAssessment preparation and completion214:0028:00Formative and summative reports
Structured Guided LearningLecture materials91:3013:30Non synchronous on line - prerecorded lectures and set reading
Scheduled Learning And Teaching ActivitiesPractical62:0012:00Present in Person structured synchronous practical
Guided Independent StudyProject work129:0029:00Main project
Scheduled Learning And Teaching ActivitiesDrop-in/surgery22:004:00Present in person drop-in
Guided Independent StudyIndependent study91:3013:30Lecture follow-up/background reading
Total100:00
Teaching Rationale And Relationship

Pre-recorded 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 provides opportunity to ask questions and receive immediate feedback. Students unable to attend PiP will be able to complete the practical work at home and will be able to receive immediate feedback through joining the drop-ins virtually.

Alternatives as described will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances.

Studentâ€™s should consult their individual timetable for up-to-date delivery information.

Assessment Methods

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

Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report1M40Individual report
Report1M60Main module project
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral PresentationMA three minute video articulating the main findings of one aspect of the coursework project.
Formative Assessments
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 assessments.

The oral presentation encourages students to focus on interpretation of statistical results, builds their skills in the presentation of statistical concepts, and provides opportunity for feedback.

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 summative assessment is fully consolidated, before the later assessment is attempted.