MAS8504 : Graduate Foundations of Statistics and Data Science (Theory & Methods)
- Offered for Year: 2025/26
- Module Leader(s): Dr James Bentham
- Lecturer: Dr Aamir Khan
- 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: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
Statistics is a fundamental discipline in Business Analytics. 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 business analytics practice.
Outline Of Syllabus
This course will introduce both classical and Bayesian approaches to statistical inference, and where appropriate contrast them with one another. Relevant notions of probability will be introduced where appropriate. Topics covered will include parametric families of models, likelihood, hypothesis testing, and p-values. We will introduce Bayes’ theorem (both continuous and discrete), as well as the practical specification of priors and computation of posteriors. Classes of common statistical models will be considered, such as linear and generalised linear models. Focus will be spent on the use of computational techniques to conduct statistical analysis (such as random sampling, Monte Carlo, Markov chain Monte Carlo, and related techniques).
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 20 | 2:00 | 40:00 | Formal Lectures |
| Guided Independent Study | Assessment preparation and completion | 2 | 10:00 | 20:00 | Completion of in-course assessments |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:30 | 2:30 | Unseen exam |
| Scheduled Learning And Teaching Activities | Lecture | 10 | 1:00 | 10:00 | Problem Classes |
| Scheduled Learning And Teaching Activities | Lecture | 5 | 1:00 | 5:00 | Revision Lectures |
| Guided Independent Study | Independent study | 45 | 1:00 | 45:00 | Revision for unseen exam |
| Guided Independent Study | Independent study | 30 | 2:00 | 60:00 | Preparation time for lectures and consolidation of material afterwards |
| Guided Independent Study | Independent study | 10 | 1:30 | 15:00 | Background reading on lectured content |
| Guided Independent Study | Independent study | 2 | 1:15 | 2:30 | Review of coursework |
| Total | 200:00 |
Jointly Taught With
| Code | Title |
|---|---|
| CSC8643 | Data Management and Exploratory Data Analysis |
| MAS8407 | Practical Statistics for Exploratory Data Analytics |
| MAS8600 | Graduate Foundations of Statistics and Data Science |
| MAS8505 | Graduate Foundations of Statistics and Data Science (Applications) |
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. Problem classes are used to help develop the students’ abilities at applying the theory to solving problems. Practical classes are used to help the students’ ability to apply the methods in practice.
The teaching methods are appropriate to allow students to develop a wide range of skills. From understanding basic concepts and facts to higher-order thinking.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 150 | 1 | A | 75 | 2.5 hour written exam, comprising a Section A and a Section B. |
Exam Pairings
| Module Code | Module Title | Semester | Comment |
|---|---|---|---|
| Graduate Foundations of Statistics and Data Science | 1 | N/A |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Prob solv exercises | 1 | M | 25 | Coursework 2. 40-minute class test, conducted during one of the timetabled one-hour lecture slots. |
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 |
|---|---|---|---|
| Prob solv exercises | 1 | M | Coursework 1. Up to 6 page typeset report based upon a set assignment comprising open-ended questions. |
Assessment Rationale And Relationship
A substantial formal unseen examination is appropriate for the assessment of the material in this module. The format of the examination will enable students to reliably demonstrate their own knowledge, understanding and application of learning outcomes.
Examination problems may require a synthesis of concepts and strategies from different sections, while they may have more than one way for solution. The examination time allows the students to test different strategies, work out examples and gather evidence for deciding on an effective strategy, while carefully articulating their ideas and explicitly citing the theory they are using.
The coursework assignments 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 summative assessment has a secondary formative purpose as well as its primary summative purpose.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MAS8504's Timetable