module specification

MA4041 - Data Analysis and Financial Mathematics (2018/19)

Module specification Module approved to run in 2018/19, but may be subject to modification
Module title Data Analysis and Financial Mathematics
Module level Certificate (04)
Credit rating for module 30
School School of Computing and Digital Media
Total study hours 300
210 hours Guided independent study
90 hours Scheduled learning & teaching activities
Assessment components
Type Weighting Qualifying mark Description
Unseen Examination 30%   test 1 (1 hour, unseen)
Unseen Examination 30%   test 2 (1 hour, unseen)
Unseen Examination 40%   test 3 (2 hours, unseen)
Running in 2018/19
Period Campus Day Time Module Leader
Year North Monday Afternoon

Module summary

This module develops the mathematical and statistical tools that are used in the mathematics of finance.  It also introduces methods of analysing data using appropriate statistical software.
This module introduces the basic terminologies used in finance and develops the mathematical techniques to solve problems in the area of finance. Descriptive statistics and statistical techniques that are useful to present, analyse and make inferences about data are also introduced. A selection of suitable software (e.g. Excel, R, SPSS) will enable students to analyse data in order to make informed decisions.


Descriptive statistics (measures of central tendency and variability); LO2,LO4
Probability and introduction to the basic statistical distributions and hypothesis testing; LO2,LO3,LO4
Applications of AP and GP in Finance;LO1
Introduction to financial measurements including simple and compound interest and the use of Excel built functions; Mathematics of finance; LO1,LO4

Balance of independent study and scheduled teaching activity

Students’ learning is supported by blended learning via face-to-face learning activities that include lectures, seminars, individual and group-based case studies and investigations and real data analysis resourced by Weblearn. There is full provision of documents related to the module in electronic format that can be accessed by students all the time. The documents include lecture notes, slides, guidance to packages, exercises, self-assessed tests, data for analysis and feedback.  Students are motivated to analyse real data sets made available to them.

Learning outcomes

On successful completion of this module, students should be able to:
LO 1.    Demonstrate an understanding of the mathematical aspects of interest as applied to financial
LO 2.    Identify different types of data, summarise and present data; use sample data to make
             inference about population parameters.
LO 3.    Understand the ideas of probability and the basic probability distributions, discrete and
LO 4.     Use an appropriate software package (such as R, SPSS, Excel) to investigate and interpret data
              and fit statistical models to a set of data.

Assessment strategy

The assessment consists of three sets of class tests.   In the first test students are tested on their understanding of the basic financial maths and summary statistics. The second test examines the understanding gained by the students regarding the basic discrete and continuous probability distributions and the various financial models. Final test will test further statistical techniques.

Formative assessments are given every week during the tutorial/seminar sessions while summative assessment feedback will be made available at the end of each test.


Core Text:
Zima, P and Brown, R (1998) Schaum’s Mathematics of Finance, McGraw-Hill.
Veaux, R. and  Vellman,P. (2004) Intro Stats,  Addison Wesley ISBN 0-201-70910-4.

Recommended Readings:
Guthrie, G and Lemon, L (2003) Mathematics of Interest and Finance, Prentice Hall.
McClave, T. and  Sincich, T. (2003) A First Course in Statistics, Prentice Hall.
Field, A. (2009) Discovering statistics using SPSS. 3rd edition.  Sage.
Daly, F. Hand, D., Jones, Lunn, M. and  McConway, K. (1995) Elements of Statistics, Addison Wesley, ISBN 0-201-42278-6.