DSC8050 : Graduate Foundations of Data Science and Artificial Intelligence (Inactive)
- Inactive for Year: 2026/27
- Module Leader(s): Dr James Bentham
- 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
The aim of this module is to give students a thorough grounding in the key approaches to conducting statistical inference, and the ability to apply these approaches practically. This module will also give students a firm grasp of key aspects of statistical computing and data science so that they can handle and analyse data confidently.
Outline Of Syllabus
This module will introduce both classical and Bayesian approaches to statistical inference, and where appropriate contrast them with one another. Relevant introductory concepts in probability and statistics will be revised at the start of the module.
Specific topics covered will include parametric families of models, likelihood, hypothesis testing, and p-values. Classes of common statistical models will be considered, such as linear and generalised linear models. Bayes theorem (both continuous and discrete) will be introduced, as well as aspects such as the practical specification of priors and computation of posteriors.
Practical aspects of computing and data science that will be covered will include data handling, exploratory data analysis, visualisation, best practices in programming (such as the design and structure of code), and the application of these techniques to common topics in statistical computing (such as linear models and generalised linear models).
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 4 | 1:00 | 4:00 | Revision lectures |
| Scheduled Learning And Teaching Activities | Lecture | 33 | 1:00 | 33:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:30 | 2:30 | Unseen exam |
| Guided Independent Study | Assessment preparation and completion | 2 | 20:00 | 40:00 | Preparation for in-class tests |
| Guided Independent Study | Assessment preparation and completion | 2 | 1:00 | 2:00 | In class test (one formative, one summative) |
| Scheduled Learning And Teaching Activities | Practical | 5 | 1:00 | 5:00 | Computer practical |
| Guided Independent Study | Independent study | 2 | 5:15 | 10:30 | Review of in-class tests and feedback |
| Guided Independent Study | Independent study | 50 | 1:00 | 50:00 | Revision for unseen exam |
| Guided Independent Study | Independent study | 1 | 30:00 | 30:00 | Preparation time for lectures and consolidation of material afterwards |
| Guided Independent Study | Independent study | 23 | 1:00 | 23:00 | Background reading on lectured content |
| Total | 200:00 |
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 the in-class tests. 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 |
|---|---|---|---|---|---|
| Digital Examination | 150 | 1 | A | 75 | Hybrid exam with NUMBAS and written questions |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Prob solv exercises | 1 | M | 25 | 40-minute class test using NUMBAS |
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 | 40-minute class test using NUMBAS |
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. Given the hybrid nature of the examination, it will be possible to assess both theoretical and practical understanding.
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 will allow the students to assess their understanding and progress through a formative assessment early in the semester, and then to carry out a similar test later in the module to assess their progress towards the later examination.
In this module, students develop their understanding of the core theory underpinning statistics, and its practical application. Summative assessment of the theory comprises the final written examination and a class-test (which has an additional formative intention).
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC8050's Timetable