Module Information

Module Identifier
Module Title
Data Analytics
Academic Year
Semester 2
Reading List
Other Staff

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Assessment Written case study report  2,000 words  40%
Semester Exam 2 Hours   Written exam  60%
Supplementary Assessment Written case study report  2,000 words  40%
Supplementary Exam 2 Hours   Written exam  60%

Learning Outcomes

On successful completion of this module students should be able to:

Apply mathematical and algebraic methods to solve problems.

Summarize data using descriptive statistics and graphical methods.

Calculate and interpret correlations, and estimate an ordinary least squares regression.

Calculate normal and binomial probabilities, and use statistical tables.

Carry out statistical tests and interpret the results.

Solve simple linear programming problems.

Brief description

This module introduces a number of quantitative techniques, and develops the use of statistical analysis at an introductory level. The importance of quantitative methods in informing analysis and good decision making for the effective running of organizations cannot be overestimated. The module provides the opportunity to build and improve numerical, mathematical and statistical skills, and apply these to practical problems in the context of finance, economics and business.


• Mathematical and algebraic methods for problem-solving
• Linear functions and equations, including simultaneous equations
• Spreadsheet applications in cost and management accounting
• Sampling techniques: random, systematic, stratified, multistage, cluster and quota
• Presentation of information using tables, charts and graphs: bar charts, line graphs, pie charts and scatter graphs
• Linear regression analysis
• Time series analysis: cyclical, trend, seasonal variation and random elements
• Trend, moving average and seasonality
• Index numbers
• Spreadsheets applications in cost and management accounting
• Sensitivity analysis and “what if” analysis
• Linear programming problems, and solutions using the graphical method.
• Probability distributions, mean and standard deviation, expected value tables
• Decision trees for multi-stage decision problems
• Sensitivity analysis and “what if” analysis

Module Skills

Skills Type Skills details
Application of Number Core to the module, Includes: Develop an easy familiarity with numerical data sources and numerical data. Apply numerical data to problem solving with care and accuracy. Assess the reasonableness of and interpret numerical solutions. Support assertions/arguments with appropriately developed and presented numerical data. Calculate and use a variety of statistics. Apply mathematical formulae.
Communication Written report of case study must be clear and well structured. Good listening skills are also essential and students will develop confidence in oral communication during the tutorials.
Improving own Learning and Performance Identify and use a range of learning resources. Structure study to accommodate intensive learning, completion of work on time and necessary preparation between lectures. Identify and distil the key issues covered by lectures, tutorials and exercises.
Information Technology Practical sessions and individual work will involve using a spreadsheet package.
Personal Development and Career planning Development of critical approach to data analysis and presentation.
Problem solving Identify the precise problem to be solved. Assess which data are pertinent to the problem. Recognize that alternative solution methods might be available. Select and apply appropriate methods for solving the problem. Assess the reasonableness of problem solutions and interpret those solutions.
Research skills Identify which information sources are available to: Facilitate module study (understanding, wider reading). Provide data which allow application of module learning in a real world context. Critically assess information.
Subject Specific Skills Purpose of module is to equip students with basic mathematical, statistical and analytical skills which can be built on in subsequent modules.
Team work Students will be encouraged to work together on questions during the example classes, in practical sessions and in the case study work.


This module is at CQFW Level 4