AF5036 - Mathematics and Statistics for Economics and Finance

What will I learn on this module?

Building on knowledge gained at level 4, you will continue to learn and increase your skills in applying a variety of practical mathematical and statistical methods specific to the disciplines of finance and economics. On the side of mathematics, this module will expand on geometric series, optimisation methods and techniques, differentiation and integration. On the side of statistics, you will review the derivation of probability distribution and introduce basic principles for machine learning. The module will address formal derivations of mathematical and statistic functions by applying theories and techniques to practical economic and finance cases and examples. Topics in this module include:
1. Review of Mathematical Functions;
2. Mathematics of Financial Returns;
3. Optimisation Methods: Linear Programming;
4. Differentiation of functions of two or more variables
5. Optimisation methods: Maxima and minima of a function of two variables
6. Integration: primitive of a function and Riemann integral;
7. Linear Algebra and Portfolio Theory
8. Probability Distributions;
9. Maximum likelihood principle and elements of Machine Learning.

How will I learn on this module?

You will learn through lectures (12 hours), workshops (24 hours) in an IT lab, tutor directed study (82 hours) and independent study (82 hours). You will be required to demonstrate high levels of engagement in understanding mathematical methods and applications. Independent learning will use both core and recommended texts to practice and further develop understanding through specific examples and applications. You can therefore expect to employ the reflective-practitioner approach to learning that is both knowledge and skills based and embedded in all delivery sessions. Directed learning will centre upon a range of activities including pre-reading, preparation for interactive activities, practice in IT lab, and use of the discussion board to learn and share knowledge. Independent learning will centre upon the students identifying and pursuing areas of interest in relation to mathematics and statistics and by providing deeper/broader knowledge and understanding of the subject through a range of learning activities that will include extended reading, reflection and practice, as well as mathematical and statistical calculations. Critical reflection on knowledge, experience and practice underpins the learning and teaching philosophy along with the explicit development of competence.

How will I be supported academically on this module?

You will be supported by a teaching and learning plan (TLP) that provides the content and structure of formal sessions, tutor-directed study and independent study. Support will be provided to you by a module lead and seminar and workshop tutors who will all provide support on a formative basis during each interactive session. Your lectures will be recorded and uploaded to the e-learning portal which you will be able to access to consolidate your knowledge and develop understanding. Your module is supported by an e-learning portal that houses lecture materials, seminar and workshop exercises and data files for use in workshop sessions. You will be provided with a wide-ranging electronic reading list that comprises of various academic reports, conference papers, video links and journal articles that will introduce you to the theory and application of the techniques introduced in the module.

What will I be expected to read on this module?

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at:
(Reading List service online guide for academic staff this containing contact details for the Reading List team –

What will I be expected to achieve?

Knowledge & Understanding (KU):
• To understand, critique and apply a variety of mathematical and statistical techniques (MLO1).
• To formulate and solve mathematical and statistical problems through the selection of specific tools and techniques (MLO2).

Intellectual / Professional skills & abilities (IPSA):

• To use statistical and mathematical models for the analysis of data (MLO3).
• To appreciate the relevance of theory for empirical applications (MLO4).

Personal Values Attributes (PVA):

• You will develop a robust analytical skillset that will enable you to analyse and interpret a wide range of issues within and outside the disciplines of economics and finance. (MLO5).

How will I be assessed?

Formative assessment:
Formative assessment will take place through discussion and reflection, discussion board activity on the e-learning platform, case study activity and in workshop sessions. Formative feedback will be provided throughout the module, particularly in relation to workshop tasks. Students should, however, be aware that formative feedback can, and will, occur in any communication with the academic tutor.

Summative assessment:
This module is assessed via two components:

1. Multiple Choice Questionnaire (MCQ) eLP/Blackboard based online test. Two 1 hour sessions. Weighing 20% overall (MLO1, MLO2, MLO5)
2. 2.5 hour open noted exam (80%). (MLO1 – MLO4, MLO5)





Module abstract

The skills you acquired in Year 1 provide you with a robust platform for understanding and applying a wide range of mathematical operations and statistical tools. This module will increase and expand your knowledge of different, more advanced analytical tools for working in economics and finance. You’ll gain more awareness and control of quantitative models and techniques, learning the intuition that lies behind key theories and models in both economics and finance from a practical perspective. The module comprises workshops where you’ll use software packages to analyse and interpret data. On its completion, you’ll be able to apply appropriate analytical tools and methods to different types of data, and to discuss and explain results concisely and consistently. By acquiring these critical skills, you’ll significantly enhance your profile both in terms of employability and entrepreneurial opportunities.

Course info

UCAS Code L101

Credits 20

Level of Study Undergraduate

Mode of Study 3 years Full Time or 4 years with a placement (sandwich)/study abroad

Department Newcastle Business School

Location City Campus, Northumbria University

City Newcastle

Start September 2024 or September 2025

Fee Information

Module Information

All information is accurate at the time of sharing. 

Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.  

Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.


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