DSC1003 : Mathematical Techniques and Computation
- Inactive for Year: 2026/27
- Module Leader(s): Dr Jean Hall
- Lecturer: Dr Jere Koskela
- 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
This module develops mathematical techniques used to model change and solve computational problems arising in data-driven and analytical contexts. You will study multivariable calculus, optimisation, and numerical methods for approximating solutions where exact analytical approaches are not feasible. The emphasis is on applying mathematical and computational methods to increasingly complex problems, providing a foundation for advanced modelling, machine learning, and large-scale data analysis.
Outline Of Syllabus
Topics covered by this module include:
Differentiation and integration
Approximation, series, convergence
Functions of several variables
Partial derivatives, gradients and critical points
Numerical methods: root finding, minimisation/maximisation.
Lagrange multipliers
Visualisation of multivariate functions.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 3 | 1:00 | 3:00 | Revision session |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:30 | 2:30 | Completion of written exam |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Preparation and completion of summative computer assessment |
| Scheduled Learning And Teaching Activities | Lecture | 30 | 1:00 | 30:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Preparation and completion of formative computer assessment |
| Structured Guided Learning | Lecture materials | 1 | 40:00 | 40:00 | Preparation for lectures and tutorials |
| Guided Independent Study | Assessment preparation and completion | 1 | 49:30 | 49:30 | Independent study and revision for final written exam |
| Guided Independent Study | Directed research and reading | 1 | 20:00 | 20:00 | Wider reading as per provided reading list |
| Scheduled Learning And Teaching Activities | Small group teaching | 15 | 1:00 | 15:00 | Group tutorials |
| Total | 200:00 |
Teaching Rationale And Relationship
This module includes an encounter with the leading edge of research.
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 | 80 | Written exam |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Computer assessment | 1 | M | 20 | Numbas or equivalent, completable in 50 minutes |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Computer assessment | M | Numbas or equivalent, completable in 50 minutes |
Assessment Rationale And Relationship
The assessment structure is designed to evaluate students’ understanding and application of calculus, optimisation, and numerical methods through a combination of coursework and examination.
The computer assessment enables students to apply numerical and computational techniques to structured mathematical problems, including approximation, optimisation, and the analysis of multivariate systems. The assessment supports the development of problem-solving skills and the practical application of numerical methods within computational contexts. The formative computer assessment provides students with an opportunity to practise core techniques and receive feedback prior to the summative assessment. (MLO2, MLO3)
The written examination assesses students’ understanding of the theoretical principles and mathematical methods developed throughout the module. Students are required to solve unseen problems, apply appropriate analytical and numerical techniques, and interpret mathematical outcomes within computational and modelling contexts. The examination assesses students’ ability to reason mathematically across the breadth of the module content and provides assurance of individual understanding. (MLO1, MLO2, MLO3)
Together, the assessments ensure students demonstrate both conceptual understanding and applied competence in mathematical and numerical methods appropriate to a Stage 1 computational mathematics module.
Alternative assessment:
For students with a Student Support Plan (SSP) who have alternative assessment as a reasonable adjustment and/or students with supported Personal Extenuating Circumstances (PEC), it may be necessary to set an alternative assessment as presented below. Due to the professional requirements of some programmes, alternative assessments may not be available even where students are eligible.
An oral examination may be offered as an alternative to the written examination where appropriate. This will normally comprise a 15-minute student-led presentation on a prepared topic from the module syllabus, followed by 15 minutes of questioning covering the wider module content. The oral examination will assess the same learning outcomes as the written examination, including mathematical reasoning, application of numerical methods, and interpretation of computational results.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC1003's Timetable