MSc Finance and Data Science

1. Demonstrate a systematic understanding of fundamental concepts and techniques in principles of finance, investment, and data science (statistics, machine learning, and AI).
2. Develop a sound understanding of the selection and application of analytical tools and software suitable for critically analysing and solving data-driven problems in finance, investment, and other relevant business contexts.
3. Effectively apply tools and knowledge to address complex issues and challenges presented by small and large data sets in finance and other real-world problems.
4. Recognise the ethical considerations involved in using data and data science.

Teaching is delivered by scheduled taught lectures (A1) and classes including seminars, tutorials, and hands-on practical sessions (A1-A2)

Learning takes place through assigned reading of the relevant literature (A1), assigned problem sets, and assessed data analyses (A1-A2), and by completion of a research project (A3-A4) with support in one of the group-a modules shown in Table ‘Module Information’ in Section ‘Programme Modules’.

Teaching follows the problem-based and research-led approaches. Additionally, all teaching and learning activities are supported by online resources including .

Assessment takes place in two formats:

There will be a range of regular formative assessment which seeks to build your skills and confidence whilst keeping you engaged with the programme, including online quizzes (A1-A2), problem sets and assessed data analyses with report (A2-A3) and presentation (A4). For example, modules such as MTHM501 and BEAM078 assess data analysis during practical sessions, expecting students to report results both in writing and through presentation. In this process, ethical use of data is a relevant consideration that should be addressed at the outset of any data processing.

Summative assessment includes written examinations (A1-A3), applied exercise, written assignments (A1-A4) (e.g., case studies, business/research projects, proposals, and essays).. For example, MTHM501 and BEAM078 coursework requires data collection, analysis and report. In this process, the ethical use of data is a highly relevant consideration to begin with.

5. Develop rigorous arguments based on critical and analytical reasoning.
6. Apply proven theoretical framework and models to rigorously analyse complex problems in finance and other data-intensive areas.
7. Provide solutions to business and financial problems using a range of established finance and data science techniques for information processing and analysis.
8. Critically appraise a wide body of empirical and theoretical research literature.
9. Use relevant databases, existing research literature and techniques to conduct a detailed investigation of problems arising in finance and other data-intensive areas.
10. Evidence critical thinking in the construction of written proposals and plan own research project with support.

Teaching and learning activities in taught lectures, seminars, tutorials, and workshops Classes, assigned exercises, practical classes accommodate and facilitate B5-B7.

Guided preparation and presentation of reports and individual or group projects require critical surveys of the existing literature (B8-9).

Class solution of assigned problems develops the use of concepts and models (B5, B7).

Academic discipline core skills and knowledge are assessed by both formative and summative assessment methods.

Specifically, formative assessment includes online quizzes (B5-B6), applied exercises and problem sets (B5-B7) and presentation (B7-B9).

Summative assessment includes written examinations (B5-B8), applied exercise, written assignments (B5-B10) (e.g., case studies, business/research projects, proposals, and reports), and presentation (B9-B10).

11. Confident Communicator: adapt and adjust both written and verbal communication styles, to meet the needs of diverse audiences.
12. Innovative problem-solver: confidently explore challenges from different perspectives, to creatively offer practical and timely solutions.
13. Proactive collaborator: actively build strong working relationships with others to have positive outcomes.
14. Digitally fluent: embrace a variety of digital technologies to critically source, process and communicate information.
15. Resilient self-advocate: develop self-awareness through a commitment to learning from experiences and taking responsibility for personal growth.
16. Critical thinker: proactively analyse and evaluate information from a variety of sources to draw independent and well-founded conclusions.
17. Globally engaged: recognise diverse individual and cultural perspectives, to communicate on interconnected world issues and sustainable decisions.

C11

MTHM501 requires you to report in writing and via presentation.

MTHM502 expects you to communicate the results of data analysis clearly in writing and verbally.

BEAM078 requires you to demonstrate skills and knowledge through group presentations.

BEAM035 expects your engagement with active in-class participation and online activities.

C12

MTHM501 requires you to apply data analysis skills to solve practical data issues such as bias and missing data problems.

MTHM502 requires you to apply statistical models and methods to inform decision-making problems.

BEAM047 requires you to apply the fundamental principles of financial valuation to a variety of financial decision-making problems.

BEAM050 expects you to analyse and evaluate quantitative problems.

C13

BEAM078 requires you to collaborate as a team to deliver group presentations.

BEAM035 requires you to develop interpersonal skills and group working through assignments on LinkedIn and active class discussion and debate.

C14

MTHM501 requires you to use R/RStudio and other software to process and analyse data.

MTHM502 requires you to use R/RStudio and other software to implement statistical and data science methods.

BEAM078 requires you to demonstrate key skills in analytical practices including data handling and statistical techniques in the context of applied empirical accounting and finance.

C15

Students in this programme are expected to manage time and tasks (submission of group work, presentations, mid-term assessments, final written exams); work independently (using resources to plan own learning, review answers and identify own learning points); adapt to change (reflecting the core difference between thought, critical analysis, use of evidence; management of ever-changing circumstances related to group work; different technical approaches such as bookkeeping procedures to process a transaction); seek and use feedback (using formative and summative feedback to develop); drive towards personal goals (reflect on input and align to personal goals).

C16

MTHM502 requires you to perform statistical analysis based on critical selection and rigorous application of a variety of models and inference methods.

BEAM078 requires you to complete multistage tasks within a defined period whilst assisted by supervision.

BEAM050 expects you to explain and discuss/compare competing financial theories and models.

C17 

MTHM501 requires you to report and present results to solve practical real-world data problems which are prevalent in the contemporaneous interconnected world.

BEAM047 expects you to critically analyse problems arising in both academic and practical contexts.

BEAM035 access empirical research literature and critically appraise it. 

C11 

MTHM501 – report and presentation from practical sessions (all module ILOs)

MTHM502 – online quizzes and examination (all module ILOs)

BEAM078 – group presentation (ILO1-6)

BEAM035 –practice short-answer questions (ILO1-8)

C12

MTHM501 – assessed data analysis and reports (all module ILOs)

MTHM502 – online quizzes and examination (100%, all module ILOs)

BEAM047 – MCQ test and written examination (ILO 1-12)

BEAM050 – MCQ (ILO1-8) and in-term test (50%, ILO1-2, 4-8)

C13

BEAM078 – group presentation (ILO1-6)

BEAM035 – LinkedIn assignment and class discussion and debate (ILO16-17)

C14

MTHM501 – hands-on practical session data analyses and an extended piece of data analysis as coursework (100%, all module ILOs)

MTHM502 – online quizzes (all module ILOs) 

BEAM078 – weekly formative quizzes (ILO1-6) and assignment (100% 4000-word individual research project, ILO1-13)

C15

C15 is very general, and across the programme it is assessed through a mixture of individual/group assignments, presentations, MCQ exams, and written examinations.

C16

MTHM502 – closed-book examination (100%, all module ILOs)

BEAM078 – assignment (100%, 4000-word individual research project, ILO1-13)

BEAM050 – final exam (50%, ILO1-8)

C17

MTHM501 – report and presentation from data analyses during practical sessions (all module ILOs)

BEAM047 – MCQ test and written examination (ILO1-12)

BEAM035 – individual project (ILO16-18)