DSC2003 : Data Modelling and Learning
- Inactive for Year: 2026/27
- Module Leader(s): Mr Axel Finke
- Lecturer: Mr Matthew Fisher
- 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 introduces the principles of learning from data through statistical and machine learning approaches. You will study how models are defined, estimated, evaluated, and applied within data-driven contexts, including how models generalise beyond observed data. The emphasis is on understanding uncertainty, assumptions, model behaviour, and performance evaluation in order to reason critically about computational and statistical modelling approaches in real-world applications.
Outline Of Syllabus
Topics covered by this module include:
Statistical inference and probabilistic modelling
Hypothesis testing and confidence intervals
Regression and predictive modelling
Categorical covariates and indicator variables
Modelling non-Gaussian outcomes
Model evaluation and performance assessment
Classification methods
Model selection and comparison
Prediction and generalisation in data-driven models
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Preparation and completion of formative computer assessment |
| Structured Guided Learning | Lecture materials | 40 | 1:00 | 40:00 | Reading through lecture materials – preparing / follow up |
| Guided Independent Study | Assessment preparation and completion | 1 | 56:30 | 56:30 | Independent study and revision for final written exam |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:30 | 2:30 | Completion of written exam |
| Scheduled Learning And Teaching Activities | Lecture | 31 | 1:00 | 31:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Preparation of completion of summative assessment |
| Guided Independent Study | Directed research and reading | 20 | 1:00 | 20:00 | Wider reading as per provided reading list |
| Scheduled Learning And Teaching Activities | Small group teaching | 10 | 1:00 | 10:00 | 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 | Closed-book written exam |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 1 | M | 20 | Project using individualised data, of upto 6 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 |
|---|---|---|---|
| Computer assessment | 1 | M | Numbas assessment, of approximately 50 mins in length. |
Assessment Rationale And Relationship
The written examination assesses students’ understanding and application of statistical and machine learning principles across the module syllabus, including model formulation, evaluation, interpretation, and prediction. The examination assesses all module learning outcomes, with particular emphasis on students’ ability to reason about modelling approaches, assumptions, performance, and limitations within statistical and data-driven contexts. (MLO1, MLO2, MLO3, MLO4)
The coursework provides students with the opportunity to apply statistical and machine learning techniques to an individualised dataset within a practical modelling workflow. This supports the development and assessment of skills in dataset preparation, model application, interpretation, and data-informed decision making within an applied computational context. (MLO2, MLO4)
The formative computer assessment provides an opportunity for students to practise core computational and analytical techniques and receive feedback in advance of the summative assessments.
The assessment structure supports both theoretical understanding and applied modelling skills, enabling students to develop confidence in the practical use, evaluation, and interpretation of statistical and machine learning approaches.
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 comprising a 15-minute prepared topic followed by 15 minutes of questions from the overall module syllabus may be offered as an alternative to the written examination where appropriate.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC2003's Timetable