MAS8605 : Industrial Dissertation in Statistics and Data Science
- Offered for Year: 2025/26
- Module Leader(s): Dr Colin Gillespie
- 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
To develop a broader knowledge of how data science and statistics are used within industry. To acquire skills, both technical and commercial, relating to how data analysis can be used to influence commercial decisions. Students will have the opportunity to develop their own specialist expertise in the project topics, and further improve their planning and communication skills.
The module will consist of two back-to-back consultancy-style projects each lasting (approximately) five-weeks. Each project will present an industry-related statistical or data science problem to all students. Support will be available with fortnightly drop-in clinics. Students must submit an individual written report by a set deadline, outlining the methods used, results obtained, and their interpretation of these results. While students can discuss the problem with their peers, all analyses and reports must be completed independently.
Outline Of Syllabus
Projects will be related to a problem in industry 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 |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 2 | 2:00 | 4:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 140 | 1:00 | 140:00 | Dissertation Writing |
| Guided Independent Study | Assessment preparation and completion | 30 | 1:00 | 30:00 | Preparation for and completion of long oral presentations |
| Guided Independent Study | Assessment preparation and completion | 20 | 1:00 | 20:00 | Preparation for and completion of short oral presentations |
| Guided Independent Study | Directed research and reading | 100 | 1:00 | 100:00 | Background Research |
| Guided Independent Study | Project work | 300 | 1:00 | 300:00 | Undertaking the technical project work |
| Scheduled Learning And Teaching Activities | Dissertation/project related supervision | 12 | 0:30 | 6:00 | Group Supervisor Meetings |
| 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.
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 |
|---|---|---|---|---|
| Dissertation | 3 | M | 40 | Up to eight page technical report (excluding appendices). |
| Dissertation | 3 | M | 40 | Up to eight page technical report (excluding appendices). |
| Oral Presentation | 3 | M | 10 | Long Oral Presentation 1 - 10 minutes |
| Oral Presentation | 3 | M | 10 | Long Oral Presentation 2 - 10 minutes |
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 |
|---|---|---|---|
| Oral Presentation | 3 | M | Short Oral Presentation 1 - up to 5 minutes |
| Oral Presentation | 3 | M | Short Oral Presentation 2 - up to 5 minutes |
Assessment Rationale And Relationship
The assessment structure, comprising a four-page report, a two-minute summary talk, and a ten-minute detailed presentation, is designed to assess a student’s skills and understanding thoroughly.
Eight-Page Report:
Evaluates the 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.
Five-Minute Summary Talk:
Tests the ability to distil complex information into key points for quick communication.
assesses skill in summarising and tailoring messages for specific audiences.
Ten-Minute Detailed Talk:
Allows for an in-depth presentation of the project, demonstrating comprehensive understanding.
Assesses oral communication, detailed explanation, and question-handling skills.
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.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MAS8605's Timetable