MAS8505 : Graduate Foundations of Statistics and Data Science (Applications)
- 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: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Aims
This module will give students a firm grasp of key aspects and best practices in statistical computing and data science for them to confidently handle and analyse data.
Outline Of Syllabus
Practical aspects of computing and data science which will be covered will include data handling, exploratory data analysis, visualisation, best practices in programming (such as the design and structure of code, documentation, and version control), and the application of these techniques to common topics in statistical computing (such as, generalised linear models).
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 2 | 20:00 | 40:00 | Completion of in-course assessments |
| Scheduled Learning And Teaching Activities | Lecture | 10 | 1:00 | 10:00 | Formal Lectures |
| Scheduled Learning And Teaching Activities | Practical | 10 | 2:00 | 20:00 | Computer Practical |
| Guided Independent Study | Independent study | 10 | 0:30 | 5:00 | Background reading on lectured content |
| Guided Independent Study | Independent study | 2 | 2:30 | 5:00 | Review of coursework |
| Guided Independent Study | Independent study | 10 | 2:00 | 20:00 | Preparation time for lectures and consolidation of material afterward |
| Total | 100:00 |
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 |
| MAS8504 | Graduate Foundations of Statistics and Data Science (Theory & Methods) |
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. 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
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Prob solv exercises | 1 | M | 100 | Coursework 2. Up to 15-page typeset report based upon a set assignment comprising open-ended questions. |
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
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/
- MAS8505's Timetable