DSC1001 : Introduction to Data Science and AI
- Inactive for Year: 2026/27
- Module Leader(s): Professor Barry Hodgson
- Lecturer: Mr Graham Cole, Professor Nick Wright, Dr Katherine James, Dr Stephen McGough
- Owning School: Computing
- 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 key ideas, applications, and emerging developments in data science and artificial intelligence. Through guided practical exploration, you will work with data, investigate contemporary AI systems, and examine how computational approaches are applied across different domains. The emphasis is on developing foundational understanding, critical awareness, and an appreciation of the opportunities and challenges associated with modern data and AI technologies.
Outline Of Syllabus
The module will include the following core topics:
Foundations and History of Data Science and AI: introduction to the development of data science, machine learning, and artificial intelligence, including major ideas, milestones, contemporary developments, and possible future directions.
Data Science and AI in Industry: introduction to how data science and AI projects are developed and applied in professional contexts, including project lifecycles, data acquisition, modelling, deployment, and communication of results.
In addition, students will study a selection of further contemporary topics. Indicative topics include:
Working with Data: practical exploration of real-world datasets, including data collection, representation, visualisation, and exploratory analysis.
Exploring Advanced AI Systems: guided exploration of contemporary AI systems, including generative and multimodal AI tools, focusing on capabilities, limitations, and reliability.
Exploring Generative AI: introduction to prompt development, evaluation of generated outputs, and critical reflection on the effective use of generative AI systems.
Data, AI, and the Changing Planet: applications of data science and AI to environmental monitoring, sustainability, and global change using observational and sensor data.
Cybersecurity and Intelligent Systems: overview of intelligent systems and AI methods used within cybersecurity, monitoring, and digital protection.
Ethics, Safety, and Responsible AI: exploration of ethical, societal, and professional issues associated with AI systems, including bias, fairness, accountability, and responsible innovation.
Simulation-based Learning: introduction to simulation-based approaches, probabilistic modelling, and the use of synthetic data within data science and AI workflows.
Extrapolation and Extreme Values: investigation of uncertainty, rare events, and reasoning beyond observed data in computational and statistical contexts.
Data-driven Astrophysics: introduction to large-scale scientific data analysis through examples from astronomy and cosmology.
Designing User-centred Data Solutions: introduction to design thinking, stakeholder requirements, and user-centred approaches to developing data-driven systems.
Explorer Robots: introduction to intelligent robotic systems operating in complex and uncertain environments using sensing, data, and AI-driven decision making.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Structured Guided Learning | Lecture materials | 22 | 1:00 | 22:00 | Preparation for lectures. |
| Guided Independent Study | Assessment preparation and completion | 25 | 1:00 | 25:00 | Review of material for oral presentation, and work associated with preparing presentation. |
| Scheduled Learning And Teaching Activities | Lecture | 11 | 2:00 | 22:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 25 | 1:00 | 25:00 | Preparation and completion of portfolio submission |
| Guided Independent Study | Assessment preparation and completion | 15 | 1:00 | 15:00 | Preparation and completion of formative report |
| Guided Independent Study | Directed research and reading | 20 | 1:00 | 20:00 | Wider reading as per provided reading list |
| Scheduled Learning And Teaching Activities | Practical | 10 | 2:00 | 20:00 | Supervised practical (lab) sessions focused on hands on implementation |
| Guided Independent Study | Independent study | 51 | 1:00 | 51:00 | Further reading associated with topics presented during lectures |
| Total | 200:00 |
Teaching Rationale And Relationship
This module includes an encounter with the leading edge of industry.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Oral Presentation | 15 | 1 | M | 50 | Students will present individually (max. 3 mins each) within groups, an assigned paper. |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Portfolio | 1 | M | 50 | Report (max. 5 pages) which includes a selected exercise from each of the 5 topics during the course. |
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 |
|---|---|---|---|
| Report | 1 | M | 2-page group submission of a potential industrial based project brief based upon representative client emails. |
Assessment Rationale And Relationship
The assessment structure combines portfolio-based coursework and oral presentation to evaluate students’ ability to engage with contemporary topics, practical applications, and emerging developments in data science and artificial intelligence. The assessments are designed to support both technical exploration and critical reflection while developing communication and analytical skills appropriate to Stage 1 study.
The portfolio assessment enables students to demonstrate practical engagement with a range of topic areas studied throughout the module. Through selected exercises and short written responses, students apply introductory data analysis and computational techniques, explore contemporary AI systems and datasets, and interpret outputs generated through data-driven approaches. The portfolio supports the assessment of practical, analytical, and reflective learning developed across the module topics. (MLO1, MLO2, MLO3, MLO5, MLO6)
The oral presentation assesses students’ ability to communicate technical and conceptual material clearly and appropriately to different audiences through discussion of an assigned paper or contemporary topic area. The presentation develops introductory skills in verbal communication, critical interpretation of technical material, and engagement with current developments in data science and artificial intelligence. (MLO3, MLO4, MLO5)
The formative group report provides students with an opportunity to engage with an authentic industrial-style scenario involving representative client communications and project requirements. This formative activity supports early development of collaborative working, problem definition, and professional communication skills prior to completion of the summative assessments.
The assessment strategy collectively supports the development of analytical, computational, communication, and ethically informed critical thinking skills while encouraging students to engage with contemporary and emerging applications of data science and artificial intelligence.
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.
The oral presentation may present barriers for some students. Where appropriate, the alternative assessment for this component will be an individual reflective written submission and/or recorded presentation designed to assess the same learning outcomes relating to communication, critical interpretation, and reflective engagement with contemporary topics.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC1001's Timetable