Module code: ECOM077

Module Overview

The first part of the module is designed to provide the necessary foundation in mathematical and statistical techniques for the study of economics at graduate level. The second part provides an introduction to programming using specialist programming software. Students will learn how to use numerical methods in the context of mathematic optimisation and data analysis.

Module provider


Module Leader

LAZOPOULOS Ioannis (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Workshop Hours: 16

Independent Learning Hours: 90

Lecture Hours: 12

Tutorial Hours: 6

Guided Learning: 11

Captured Content: 15

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

The module encompasses salient aspects of the basic algebra, probability theory, calculus and optimisation, as well as data clearing and analysis, code parallelization and web scraping.

Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test CLASS TEST (1 HR) 10
Coursework Take Home Problem Set 1 10
Coursework Take Home Problem Set 2 10
Coursework Take Home Problem Set 3 30
Examination FINAL EXAMINATION (60 MIN) 40

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the modules learning outcomes and, by association, developed their digital capabilities, global intelligence, resourcefulness, and employability, among other module attributes.

Thus, the summative assessment for this module consists of:

A class test worth 10% of the final grade

Two pieces of coursework, each worth 10% of the final grade

A final piece of coursework worth 30% of the final grade

A final exam worth 40% of the final grade

Formative assessment and feedback Feedback is provided in the form of verbal feedback through discussions of open-ended questions during lectures or questions provided in problem sets during tutorials. Students also receive detailed solutions to questions/exercises against which they can compare their answers to enhance their understanding. Solutions to the class test and coursework are released at the end of the assessment which includes feedback on student performance and advice on how to improve where appropriate.

Module aims

  • The module aims to equip students with the essential quantitative skills required for studying economics at post-graduate level in economics along with programming skills in data analysis which are imperative for any career path in Economics or Finance.

Learning outcomes

Attributes Developed
001 Evidence the capacity to solve quantitative problems in under time-constraint CKPT
002 Engage with the management of datasets and apply mathematical and statistical analysis tools in a practical context CKPT
003 Demonstrate effective work in a small, international team towards a shared goal CKPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to ensure that students achieve the module¿s learning outcomes. Students have access to pre-recorded videos (11 hours) summarising the learning outcomes of the week and highlighting important concepts ahead of the weekly in-person sessions. These sessions comprise of lectures (22 hours) which are recorded and released, and tutorials over the first 6 weeks (6 hours) which are not recorded. Lectures emphasise the understanding of theory, whereas workshops are designed to be interactive involving discussion in solving various problem sets. An additional component involving weekly guided learning (11 hours) is included.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: ECOM077

Other information

In line with the University's curriculum framework, the School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas: Digital Capabilities Through learning outcome 2 Resourcefulness and Resilience Through learning outcome 2 Employability Through learning outcome 3 Global and Cultural Intelligence Through learning outcome 3

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Economics (Econometrics and Big Data) MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Economics (Policy Evaluation and Data Analysis) MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Economics (Macroeconomics and Financial Markets) MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Economics (International Economics) MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Economics MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Financial Data Science MSc 1 Optional A weighted aggregate mark of 40% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.