Why study Modern Applications of Engineering Mathematics?

In the current climate employers are looking for graduates with a broader range of skills, able to meet the latest needs of an industry where technology is changing in unpredictable ways. Due to their inherently strong mathematical background, engineers are ideally placed to thrive in such a rapidly evolving environment where the focus shifts more and more towards the efficient analysis of a large amount of data in order to forecast future behavior (of processes, markets, prices among others). It is predicted that big data will play a major role in shaping the design of materials, products, systems, innovations from heavy industry to energy, from environmental engineering to genomics and healthcare.

The aim of this Minor is to provide students with a broad set of tools that will strengthen and diversify their engineering skills and enhance their employability prospects across multiple business sectors. Invited lectures from guest speakers will complement the course by providing their real-world perspective and views on how mathematical tools are used in different businesses.

What will you learn?

On completion of this Minor, you will be able to:

  • Understand the basic concepts underpinning financial engineering
  • Perform calculations using Quantitative Finance techniques
  • Handle large datasets (Big Data) from a variety of applications
  • Identify appropriate dimensionality reduction techniques to apply depending on the nature of the data being analysed
  • Understand the concepts and relevance of stochastic calculus
  • Be able to solve high-dimensionality problems using appropriate Monte Carlo techniques
  • Understand the concept of uncertainty in the model output, identify and use appropriate sensitivity analysis methods to study the allocation of uncertainty

Modules

Module 1 (Year 2, Term 2) – Engineering Mathematics in Finance

This module aims to introduce students to the basic principles of “Financial Engineering” and discuss how it links with classes of problems commonly met in traditional Engineering applications.

On successfully completing the module, students will be able to:

    • Recognise the connections between mathematics, engineering and how mathematical ideas are embedded in a Financial Engineering context
    • Understand the basic concepts of Quantitative Finance & Financial Econometrics
    • Perform basic calculations using Quantitative Finance techniques
    • Measure and forecast the volatility of bond returns
    • Model long-term relationships between prices and exchange rates
    • Relate the behaviour of the output of mathematical models to the underlying conceptual models of interest
    • Solve optimisation problems in Finance and Economics
    • Present and interpret quantitative results in effective and appropriate ways to varied audiences, including non-mathematical or engineering audiences

Module Outline – Engineering Mathematics in Finance

Module 2 (Year 3, Term 1) – Data Mining and Analysis

This module will provide a “roadmap” of computational and statistical techniques able to handle large datasets for a wide variety of applications.

On successfully completing the module, students will be able to:

  • Determine whether a real world problem has a data mining solution
  • Display an understanding of different data mining tasks and the algorithms most appropriate for addressing them.
  • Critique the results of a data mining exercise.
  • Apply the techniques of clustering, classification, association finding, feature selection and visualisation on real world data
  • Apply data mining software and toolkits in a range of applications
  • Carry out problem solving both collaboratively in a team and independently on a piece of practical work that requires the application of data mining techniques.
  • Present and interpret quantitative results in effective and appropriate ways to varied audiences, including non-mathematical or engineering audiences.

Module Outline – Data Mining and Analysis

Module 3 (Year 3, Term 2) – Stochastic Calculus and Uncertainty Analysis

This module will introduce the concepts and theories of Stochastic calculus and the use of Monte Carlo techniques for the solution of high dimensional problems prevalent in modern Engineering (and Finance) applications.

On successfully completing the module, students will be able to:

  • Recognise the connections between stochastic processes, stochastic calculus and how stochasticity is embedded in real-life applications;
  • Understand the basic concepts of Stochastic Calculus
  • Perform integration of simple stochastic processes
  • Perform integration of continuous stochastic processes
  • Understand the operating principles and limitations of Random Number Generators
  • Identify appropriate methods for and perform Sensitivity Analysis on real-life models sourced from various disciplines
  • Present and interpret quantitative results in effective and appropriate ways to varied audiences, including non-mathematical or engineering audiences.

Module Outline – Stochastic Calculus and Uncertainty Analysis

Exclusions

Students who have NOT successfully completed ENGS103P or MATH6301 (for CS students) and an appropriate Intermediate level (Year 2) Mathematics or Statistics module.

Lead Academics

Dr Alexandros Kiparissides and Dr Vasos Pavlika
Dept Biochemical Engineering
Email: ku.ca1529485559.lcu@1529485559sedis1529485559sirap1529485559ik.xe1529485559la1529485559 and/or: ku.ca1529485559.lcu@1529485559akilv1529485559ap.v1529485559

Choose your IEP Minor

Please select your preferred Minor via IEP Minors Moodle Poll