SSST Subjects


Computer Science


Information Systems


Programme(s) where module is offered

  • BSc Computer Science with Electrical Engineering
  • BSc Computer Science with Economics
  • BSc Computer Science with Management
  • BSc Computer Science with International Relations
  • BSc Computer Science with Political Science
  • BSc Information Systems with Electrical Engineering
  • BSc Information Systems with Economics
  • BSc Information Systems with Management
  • BSc Information Systems with International Relations
  • BSc Information Systems with Political Science;

Status (core, option, free choice)



FHEQ Level



Unit Value



Semester taught



Pre-Requisite Modules or Qualifications



Module Code

MATH 260


Module coordinator

Dr. Mirna Udovicic


Applicable From



Educational Aims of the Module

  • This module will start with graphical interpretation and display of data.
  • Describing patterns and departures from patters including discussion of central tendencies and variability for both univariate and bivariate data will be studied.
  • Exploring bivariate data will include correlation, linearity, least-squares regression lines, and residuals analysis if times permits.
  • Some fundamental topics in probability need to be covered: interpreting probability together with discrete random variables (including binomial and geometric and expected values and variability of those), of continuous random variables such as normal distribution, and sampling distributions.
  • This module will also cover statistical inference, estimating population parameters, testing hypothesis and confidence intervals.
  • The module will also touch upon sampling and experimentation techniques of planning and conducting a study.

Module Outline/Syllabus

  • Statistics, Data, and Statistical Thinking
  • Methods for Describing Sets of Data
  • Probability
  • Random Variables and Probability Distribution
  • Inferences Based on a Single Sample
  • Inferences Based on a Two Samples
  • Design of Experiments and Analysis of Variance
  • Categorical Data Analysis

Student Engagement Hours

Type Number per Term Duration Total Time
Lectures 15 2 hours 30 hours
Tutorials 15 2 hours 30 hours
Total Guided/Independent Learning Hours 40
Total Contact Hours 60
Total Engagement Hours 100

Assessment Method Summary

Type Number Required Duration / Length Weighting Timing / Submission Deadline
Final exam 1 180 minutes 50% End of semester
Mid-term exam 1 90 minutes 20% Week 8
Project (group) 1 2,000 words 10% Week 13
Test 2 60 minutes 20% Weeks 4 and 13

Module Outcomes

Teaching and Learning Strategy:

  • Interactive lectures on module material.
  • Tutorials provide series of development exercises and solutions to illustrate the theory. 
  • Group project enables students to develop research skills and apply the gained knowledge on a concrete problem through conducting research, analysing and presenting the data and project results. 
  • Practical demonstrations 
  • Interactive lectures 
  • Weekly Lab exercises with tutor-lead support will allow students to gain hands on skills in data analysis 
  • Group project 
  • Tutorials 
  • Project 

Assessment Strategy

  • Final Exam 
  • Mid-term exam 
  • Test 
  • Project 

Key Texts and/or other learning materials

Set text

  • James T. McClave, P. George Benson and Terry Sincich.(2013) Statistics for Business and Economics, 12th edition, Pearson.

Supplementary Materials

  • Field. Discovering Statistics using SPSS, 3rd edition, Sage.
  • Newbold, P., et al., (2015), Statistics for Business and Economics, Pearson
  • Hand, D., (2008), Statistics: A short Introduction, Oxford University Press
  • Urdan, T., (2010), Statistics in Plain English, 3rd Edition, Routledge
  • Scientific Research, (2015), Open Journal of Statistics, [online], (accessed 25th November 2015)

Please note

This specification provides a concise summary of the main features of the module and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided.  More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each module and programme can be found in the departmental or programme handbook.  The accuracy of the information contained in this document is reviewed annually by the University of Buckingham and may be checked by the Quality Assurance Agency.

Date of Production : Autumn 2016

Date approved by School Learning and Teaching Committee: 28th September 2016

Date approved by School Board of Study : 12th October 2016

Date approved by University Learning and Teaching Committee: 2nd November 2016

Date of Annual Review: December 2017


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