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Finance and Data Analytics

Course overview

(MSc) Master of Science
Finance and Data Analytics
Current
University of Essex
University of Essex
Essex Business School
Colchester Campus
Masters
Full-time
MSC N3G312
http://www.essex.ac.uk/students/exams-and-coursework/ppg/pgt/assess-rules.aspx
22/06/2018

A 2:2 degree containing core modules relating to Mathematics (calculus), quantitative finance/methods or Econometrics (probability, statistics) (also see key modules)

    Key modules

    Behavioural finance, Computer science, Corporate Finance, Derivative instruments / Futures / Options, Economics, Empirical Finance, Engineering (Electrical engineering better than civil/mechanical), Foundations of Finance, Finance/International Finance (NOT Financial Reporting), Financial Economics, Financial Modelling, Mathematics, Operational Research, Portfolio Analysis, Pricing of Securities and Futures, Risk Management, Statistics

If English is not your first language, we require IELTS 6.5 overall with a minimum component score of 5.5

Additional Notes

The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.

External Examiners

Prof Donal Gregory McKillop
Queen’s University Belfast
Professor of Financial Services

External Examiners provide an independent overview of our courses, offering their expertise and help towards our continual improvement of course content, teaching, learning, and assessment. External Examiners are normally academics from other higher education institutions, but may be from the industry, business or the profession as appropriate for the course. They comment on how well courses align with national standards, and on how well the teaching, learning and assessment methods allow students to develop and demonstrate the relevant knowledge and skills needed to achieve their awards. External Examiners who are responsible for awards are key members of Boards of Examiners. These boards make decisions about student progression within their course and about whether students can receive their final award.

eNROL, the module enrolment system, is now open until Monday 21 October 2019 8:59AM, for students wishing to make changes to their module options.

Key

Core You must take this module You must pass this module. No failure can be permitted.
Core with Options You can choose which module to study
Compulsory You must take this module There may be limited opportunities to continue on the course/be eligible for the degree if you fail.
Compulsory with Options You can choose which module to study
Optional You can choose which module to study

Year 1 - 2019/20

Exit Award Status
Component Number Module Code Module Title Status Credits PG Diploma PG Certificate
01 BE982-7-FY MSc Finance and Investment: Dissertation Core 40 Optional
02 BE953-7-AU Research Methods in Finance: Empirical Methods in Finance Core 20 Optional Optional
03 BE367-7-SP Data Analytics in Finance Core 20 Optional Optional
04 BE364-7-SP Trading Global Financial Markets Compulsory 20 Optional Optional
05 BE354-7-AU Portfolio Management Compulsory 20 Optional Optional
06 Option from list Optional 20 Optional Optional
07 Option from list Optional 20 Optional Optional
08 Option from list Optional 20 Optional Optional

Exit awards

A module is given one of the following statuses: 'core' – meaning it must be taken and passed; 'compulsory' – meaning it must be taken; or 'optional' – meaning that students can choose the module from a designated list. The rules of assessment may allow for limited condonement of fails in 'compulsory' or 'optional' modules, but 'core' modules cannot be failed. The status of the module may be different in any exit awards which are available for the course. Exam Boards will consider students' eligibility for an exit award if they fail the main award or do not complete their studies.

Programme aims

To provide students with the opportunity to broaden and deepen their knowledge of advanced concepts in finance and data analytics.

To provide students with the opportunity to analyse the role of financial information and data.

To equip students with a knowledge of advanced research methodologies covering quantitative approaches to empirical research.

To provide students with the advanced knowledge and skills to enable them to proceed to independent, self-directed research.

To develop students' critical and analytical skills which will prepare them for employment in the financial or business sectors or an education environment.


Learning outcomes and learning, teaching and assessment methods

On successful completion of the programme a graduate should demonstrate knowledge and skills as follows:

A: Knowledge and understanding

A1 Role and nature of advanced concepts and theoretical models in finance and data analytics
A2 Competing perspectives and associated empirical evidence relating to finance and data analytics issues.
A3 Epistemological and social scientific influences and interpretations of finance and data analytics
A4 An in-depth understanding of the various research methodologies available to investigate finance and data analytics issues and the influences of these methods on the understandings generated.
A5 In-depth understanding of particular areas in which the student has chosen to specialise.
Learning Methods: Outcomes A1-A5 are acquired through lectures, seminars, individual tasks, and directed independent study. The development of the dissertation in consultation with a supervisor provides an additional opportunity for achieving learning outcomes A1-A5.

Lectures and seminars introduce the required theories and understanding to facilitate exploration of the character, contexts, practices and interpretations of finance and investment related issues while demonstrating and encouraging a critical and reflexive approach.

Directed independent study and reading, along with individual tasks, facilitate further exploration of the relevant areas.
Students are expected to extend and enhance the knowledge and understanding they acquire from lectures and classes by regularly consulting library and journal materials relating to course.

Assessment Methods: Outcomes A1-A5 are informally assessed via oral presentations. The associated informal feedback provided enable students to explore and enhance their understandings and develop presentation skills.

Formal:
Outcomes A1-A5 are formally assessed via unseen written examinations and coursework assignments.

B: Intellectual and cognitive skills

B1 Capacity to appraise theoretical ideas.
B2 Assimilate and synthesise advanced theories and concepts from a variety of relevant frameworks.
B3 Formulate logical and coherent arguments
B4 Interpret and critically evaluate empirical evidence.
B5 Plan and undertake a substantial piece of independent research.
Learning Methods: Outcomes B1-B5 are acquired through lectures, seminars, individual tasks, and directed independent study. The development of the dissertation in consultation with a supervisor provides an additional opportunity for achieving learning outcomes B1-B5.

Lectures and seminars introduce the required theories and understanding to facilitate exploration of the character, contexts, practices and interpretations of finance and data analytics related issues while demonstrating and encouraging a critical and reflexive approach.

Directed independent study and reading, along with individual tasks, facilitate further exploration of the relevant areas.
Students are expected to extend and enhance the knowledge and understanding they acquire from lectures and classes by regularly consulting library and journal materials relating to course.

Assessment Methods: Outcomes B1-B5 are informally assessed via oral presentations. The associated informal feedback provided enable students to explore and enhance their understandings and develop presentation skills.

Formal:
Outcomes B1-B4 are formally assessed via unseen written examinations, coursework assignments and the dissertation.

Outcome B5 is assessed through the dissertation.

C: Practical skills

C1 Analyse and evaluate financial information
C2 Evaluate the strengths and limitations of different approaches to analysing financial information and data
C3 Analyse and evaluate financial data
C4 Gathering and processing information from different sources, e.g. doing a bibliographic search in the library, accessing material from online databases and locating and downloading appropriate foreign language materials from the Web
C5 Record and summarize transactions and other economic events
Learning Methods: Skills C1 to C4 are acquired and enhanced primarily through the work that students do for their courses, although lectures provide a means for teachers to demonstrate these skills through examples.

Skill C5 is acquired through the work that students do for their dissertation. The dissertation further provides ample opportunity for students to acquire and develop skills C1 to C4.

Assessment Methods: Informal:
Skills C1 to C4 are informally assessed through presentations along with the associated informal feedback. These further enable students to explore and enhance their understandings, and develop research and presentation skills.

Formal:
Skills C1 to C4 are formally assessed via unseen written examinations and coursework assignments. These facilitate demonstration of knowledge of the relevant financial and investment theories and of a critical and reflexive approach to empirical evidence.

Skill C5 is assessed through the dissertation

D: Key skills

D1 Communicate ideas and arguments in a coherent and effective manner.
D2 Use information technology, such as word processing, databases, the web and econometric packages, to download and analyse financial and economic data.
D3 Use of mathematical techniques to construct financial models
D4 Application of stat Analytics/financial reasoning to deal with complex issues
D5 Time management, task prioritisation and working to deadlines.
D6 Ability to organise one's own study plan; ability to reflect on his/her experience and adapt in response to feedback; ability to appreciate the role of supporting research
Learning Methods:
Assessment Methods:


Note

The University makes every effort to ensure that this information on its programme specification is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to courses, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements, industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to courses may for example consist of variations to the content and method of delivery of programmes, courses and other services, to discontinue programmes, courses and other services and to merge or combine programmes or courses. The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications.

The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.

Should you have any questions about programme specifications, please contact Course Records, Quality and Academic Development; email: crt@essex.ac.uk.