(MSc) Master of Science
Financial Technology (Computer Science)
Current
University of Essex
University of Essex
Computer Science and Electronic Engineering (School of)
Colchester Campus
Masters
Full-time
MSC N3CL12
10/05/2023
Details
Professional accreditation
None
Admission criteria
Applicants with a 2:2 degree in one of the following subjects (with no module requirements):
- Engineering
- Finance
- Economics
- Maths
- Statistics
- Physics
- Computer Science
We will consider applicants with any other 2:2 degree or above which include one or more modules from this list:
At least one Maths or Econometrics modules such as:
- Econometrics
- Calculus
- Statistics
- Differential Equations
- Probability
- Stochastic Processes
IELTS (International English Language Testing System) code
IELTS 6.0 overall with a minimum component score of 5.5
If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.
Course qualifiers
A course qualifier is a bracketed addition to your course title to denote a specialisation or pathway that you have achieved via the completion of specific modules during your course. The
specific module requirements for each qualifier title are noted below. Eligibility for any selected qualifier will be determined by the department and confirmed by the final year Board of
Examiners. If the required modules are not successfully completed, your course title will remain as described above without any bracketed addition. Selection of a course qualifier is
optional and student can register preferences or opt-out via Online Module Enrolment (eNROL).
None
Rules of assessment
Rules of assessment are the rules, principles and frameworks which the University uses to calculate your course progression and final results.
Additional notes
None
External examiners
Dr Anna Jordanous
Senior Lecturer
University of Kent
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.
Programme aims
From mobile banking to cryptocurrencies, from robo-advisors to copy trading, the financial services sector is transforming rapidly. The MSc Financial Technology (Computer Science) provides an interdisciplinary programme covering a range of crucial skills required for working in this sector, making the most of the research and teaching strengths at Essex. Students will learn about topics including microeconomics and big data, software development and the underpinnings of the financial system. Students will benefit from the University’s proximity to London as well as its connections with employers. Students will mingle not only with others specializing in computer science, but also those specializing on the economics of Financial Technology side as well as in business strategy.
Designed for students seeking a career in the finance industry, in particular the financial technology sector. The course will equip the students with both theoretical and technical skills that are specific for the finance industry. The course will also equip the students with transferable skills such as the ability to develop and present an argument, and the ability to work independently and in groups.
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: Comprehensive knowledge of fundamental principles and tools used in the financial technology sector.
A2: Detailed knowledge of core methods of analysis of financial markets, including theoretical frameworks and use of quantitative data.
A3: Systematic understanding of the relationships between principles and real world applications of those principles
A4: Understanding of the principles of (data-driven) algorithms and their application on financial industry
A5: Critical awareness of the significance of alternative approaches
Learning methods
Outcomes A1-A5 are acquired through lectures, classes, and related coursework.
The development of the dissertation in consultation with a supervisor provides an additional opportunity for the acquisition of outcomes.
Lectures are used to present material - ideas, data and arguments - in a clear and structured manner. This clarity and structure will help ensure that the course is inclusive to all students regardless of learning style and needs.
Lectures are also used to stimulate students' interest in learning.
Classes and preparation for lectures and classes, provide an opportunity for students to develop their knowledge and understanding of the content of the modules.
Preparation for assignments and for examinations aids students in developing this knowledge and understanding.
Students are expected to extend and enhance the knowledge and understanding they acquire from lectures and classes by regularly consulting library materials relating to the course. Library resources are especially designed to be accessible to all students regardless of background and learning needs.
Assessment methods
Outcomes A1-A5 are assessed throughout the modules comprising the degree by means of written examinations and coursework
The dissertation provides a further opportunity to assess outcomes A1-A5.
The usual range of approaches will be taken to ensure that assessment is inclusive, including allowing for extra exam time where necessary, and taking into account extenuating circumstances.
B: Intellectual and cognitive skills
B1: Logically assess particular financial, economic and computational problems and choose appropriate methods for their solution among basic tools of analysis.
B2: Exercise critical judgement in assessing different and competing theories and methods and appraising their merits
B3: Formulate logical and coherent arguments.
B4: Construct reasoned, informed and concise descriptions and assessments of ideas
Learning methods
Skills B1-B4 are acquired and enhanced primarily through the work that students do for their modules, although lectures provide a means for teachers to demonstrate these skills through examples and applications.
Student preparation involves the reading, interpretation and evaluation of the relevant material including the relevant literature.
Teachers provide written and/or verbal feedback on student work through comment and discussion.
In addition, teachers engage students outside the classroom through academic support hours, appointments, and email.
Inclusivity is ensured in the following ways: lecturers and other teachers are informed at the start of the term about students with special needs; student voice groups allow representatives to discuss issues surrounding learning for minorities.
Assessment methods
Skills B1-B4 are assessed throughout the modules comprising the degree by means of written examinations and coursework.
The dissertation provides a further opportunity to assess skills B1-B4.
The usual range of approaches will be taken to ensure that assessment is inclusive, including allowing for extra exam time where necessary, and taking into account extenuating circumstances
C: Practical skills
C1: Identify, select and gather information using relevant sources, including the library and online searches
C2: Develop expertise in the software used widely in financial technology applications, like python and MATLAB.
C3: Organise ideas in a systematic and critical fashion; in computer code, in mathematical language and in writing.
C4: Use and apply the right terminology and concepts
C5: Apply quantitative methods to independently solve problems in real world situations
Learning methods
Skills C1-C5 are acquired and enhanced primarily through the work that students do for their modules, as well as for their dissertation.
Lectures also provide a means of teachers demonstrating these skills through examples and applications.
Assessment methods
Skills C1-C4 are assessed throughout the modules comprising the degree by means of written examinations and coursework, including the MSc dissertation.
Skills C1 and C2 are also informally assessed by student's preparation for each module.
Skill C5 is particularly assessed through the dissertation, but is also assessed in certain courses through written assignments.
D: Key skills
D1: Communication in writing, using appropriate terminology and technical language
D2: Production of a word-processed coursework. Development of web-skills. Numerical and computational skills using Excel, Python, MATLAB, and econometric packages, e.g. Eviews and Stata.
D3: Use of mathematical techniques to construct financial models
D4: Application of logical reasoning and quantitative techniques to address issues in real world financial situations
D5: Class material can involve group working in particular subjects.
D6: Capacity to:
(a) organise and implement a plan of independent study;
(b) reflect on his or her own learning experience and adapt in response to feedback; and
(c) recognise when he or she needs to learn more and appreciate the role of additional research
Learning methods
Students are guided in acquiring skills D1-D6 through lectures, labs and individual advice from teachers. These skills are further developed as students pursue the learning activities associated with their modules and in the MSc dissertation. Students also have the opportunity to develop skills in working in groups through their participation in certain module-related activities, e.g. classroom experiments.
Assessment methods
Skills D1-D6 are assessed throughout the modules comprising the degree by means of examinations and coursework, including the MSc dissertation.