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• Inactive for Year: 2026/27

• Module Leader(s): Dr Eftychia Palamida
• Lecturer: Dr Aamir Khan

• Owning School: Â鶹´«Ã½ Business School
• Teaching Location: Â鶹´«Ã½ City Campus

Semesters

Your programme is made up of credits, the total differs on programme to programme.

Aims

The module aims to provide students with the opportunity to undertake individual independent research in data science and entrepreneurship related topics. Students will engage in putting their specialist skills, knowledge, and understanding into practice through the medium of a significant individual written dissertation. Specifically, the module aims to equip students with the following Data Science and Entrepreneurship related key knowledge and skills:

• To deepen the knowledge and skills acquired in the programme through practice.

• To enhance research skills and awareness of the professional literature.

• To develop an awareness of the open problems in both disciplines.

• To develop their own specialist expertise in the dissertation topic.

Outline Of Syllabus

“Dissertation Projects” involve working within the university’s established research groups in applied data science and entrepreneurship. Supervisors will provide advice on the approaches and methods that are best suited to the subject under investigation; on collection/analysis of data; and guidance in producing a well-written dissertation.

Students will be required to complete a “Research Proposal” document as part of the project planning and design process.

Students present their findings as a full written dissertation.

Guidance on the style and content of an academic dissertation will be provided by means of workshops and through the supervisor.

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

An initial Lecture along with workshops will be offered to support students’ involvement in research design and understanding of a research project. Students will be assigned an individual dissertation supervisor.


The dissertation supervisor will guide and support the student through the various stages of conducting their research study. Dissertation supervision will be a combination of one to one and small group dissertation meetings. Small group dissertation meetings will also offer students the opportunity to engage in peer learning.


The teaching methods for this module are designed to align with the learning outcomes as follows:


--Lectures - Provide a foundational understanding of Entrepreneurship and Data Science concepts, essential for all further learning and application.


--Workshops: Provide a foundational understanding of Entrepreneurship and Quantitative (statistical-data science)/Qualitative Research Methods concepts, essential for all further learning and application.


--Group/Individual 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 entrepreneurial context, communicate findings effectively, and understand the commercial implications of their work (theoretical and practical value of the research project).

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment

Zero Weighted Pass/Fail Assessments

Assessment Rationale And Relationship

The research proposal provides a means of assessing the project aims, methodology and planning.

The dissertation is the written evidence of the student's ability to conduct an independent research project focused on a selected topic relevant to the core themes of data science and entrepreneurship.

Reading Lists

Timetable

• Timetable Website: