DSC8101 : Case Study Project in Applied Data Science
- Offered for Year: 2025/26
- Module Leader(s): Professor Murray Pollock
- 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 3 Credit Value: | 60 |
| ECTS Credits: | 30.0 |
| European Credit Transfer System | |
Aims
This module aims to develop a critical and applied understanding of how data science and statistical methodologies are employed within real-world and disciplinary-specific contexts. Students will acquire advanced technical and/or commercially oriented skills, enabling them to apply data-driven approaches to inform strategic and operational decision-making within professional environments.
Through a consultancy-style, industry-informed group project, students will collaboratively investigate a complex data science or applied statistical problem. They will produce a scoping document and deliver a group pitch, demonstrating analytical planning, problem structuring, and stakeholder engagement. Regular academic support will be available via fortnightly drop-in clinics.
Building on the group-based foundation, students will subsequently pursue an independent line of inquiry, applying advanced analytical techniques to a self-directed project that extends the group work into a specialised area of individual interest. The module culminates in a substantial individual report, in which students will critically evaluate their chosen methods, present and interpret their findings, and reflect on the implications of their analysis. While peer discussion is encouraged in the early stages, all final analyses and submissions must be conducted independently. Ongoing academic support will continue throughout the individual project phase.
Outline Of Syllabus
Projects will be related to a real-world applied data science problem which can be tackled by appropriate methods in data science and statistics.
In every project there will be a practical component and a strong design, programming and/or analytic element.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 40 | 2:00 | 80:00 | Refresh of background literature |
| Guided Independent Study | Assessment preparation and completion | 70 | 2:00 | 140:00 | Dissertation Writing |
| Guided Independent Study | Assessment preparation and completion | 20 | 1:00 | 20:00 | Preparation for and completion of formative report |
| Scheduled Learning And Teaching Activities | Lecture | 2 | 2:00 | 4:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 20 | 1:00 | 20:00 | Preparation for and completion of group report |
| Guided Independent Study | Assessment preparation and completion | 20 | 1:00 | 20:00 | Preparation for and completion of group oral presentation |
| Guided Independent Study | Project work | 100 | 3:00 | 300:00 | Project work |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 10 | 1:00 | 10:00 | Student drop-in sessions for technical support |
| Scheduled Learning And Teaching Activities | Dissertation/project related supervision | 12 | 0:30 | 6:00 | Supervisor Meetings (8 group and 4 individual) |
| Total | 600:00 |
Teaching Rationale And Relationship
The teaching methods for this module are designed to align with the learning outcomes as follows:
Lectures - Provide a foundational understanding of statistical and data science concepts, essential for all further learning and application.
Drop in sessions - Offer informal support sessions on technical aspects which students can access as required.
Supervision: Offer personalised guidance and feedback, aiding students in refining their problem-solving approaches and communication skills.
Independent Work - Develop practical skills through self-directed study and application, fostering critical thinking, data analysis proficiency, project management, and report writing.
This structure ensures students gain both theoretical knowledge and practical expertise, preparing them to apply data science concepts in an industrial context, communicate findings effectively, and understand the commercial implications of their work
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 3 | M | 20 | Up to ten page group technical report (excluding appendices). Refer to additional guidance for specific discipline requirements |
| Oral Presentation | 3 | M | 20 | Group Presentation (25 mins) |
| Report | 3 | M | 60 | Up to twelve page individual report (excluding appendices). Refer to additional guidance for specific discipline requirements |
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 | 3 | M | Short report (max 1 page) outlining the intended individual direction of the student |
Assessment Rationale And Relationship
The assessment structure comprises a mix of group and individual assessment to support students in developing their knowledge and application of techniques. The group report 1 is used to provide a basis for the group 5min presentation summarizing the approach to scoping and problem definition. An individual formative assessment then allows the student to explore individual plans with the supervisor before completing the ten-page individual report on analysis and recommendations is designed to assess a student’s skills and understanding thoroughly
Group Report:
Evaluates the group’s ability to conduct detailed analyses and communicate findings in a structured written format. Assesses proficiency in report writing and understanding of statistical and data science concepts.
Group presentation
Allows for an in-depth presentation of the project planning and scoping exercise demonstrating comprehensive knowledge and understanding. Assesses oral communication, detailed explanation, and question-handling skills.
Individual report
Assesses the students ability to extend the original group aspects to a specific area or technique and demonstrate their knowledge and ability to communicate their research and findings in a professional manner.
This assessment structure ensures students can independently research, analyse data, and effectively present their findings in both written and oral formats, aligning with the module's learning outcomes
Formative Report
Allows the student to outline their individual approach and receive feedback on their proposal.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC8101's Timetable