This module is not currently running in 2024 to 2025.
The module introduces students to data analysis and statistics techniques that are important in the application of computers to business and industry. This includes summarising data, using measures of central tendency and dispersion, and effective graphical representations. Various probability models, including normal and binomial distributions, sampling and inference and predictive techniques are introduced. Regression and time series analysis also covered, along with what-if analysis tools.
The module teaches students to program in software such as Matlab and/or create spreadsheets using VBA scripting. These tools are used to handle data, variables and arrays, to display output data using built-in functions and develop new functions. Various problem-solving techniques and plotting for data visualisation are introduced.
Total contact hours: 28
Private study hours: 122
Total study hours: 150
13.1 Main assessment methods
Assessment 1 - Excel Fundamentals and What-if Analysis (20%)
Assessment 2 - Linear Programming & Data Analysis (20%)
Assessment 3 - Using Matlab (10%)
Assessmenr 4 - Data Analysis Project (50%)
13.2 Reassessment methods
Reassessment Instrument: 100% coursework
UpSkill Learning, MATLAB - Programming with MATLAB for Beginners: A Practical Introduction To Programming And Problem Solving (MATLAB for Engineers, MATLAB for Scientists, MATLAB Programming for Dummies), 2016
Clarke, G. M, A Basic Course In Statistics, 2004
Jelen, B, Microsoft Excel 2010 in Depth, Que, 2010
Jelen, B, VBA and Macros: Microsoft Excel 2010, Que, 2010
Albright, SC, VBA for Modelers: Developing Decision Support Systems with
Microsoft Office Excel, South Western Educational Publishing, 2011
Hillier, FS, Introduction to Operational Research, McGraw-Hill, 2009
Powell, SG, the Art of Modelling with Spreadsheets, John Wiley & Sons, 2010
See the library reading list for this module (Medway)
8. The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
8.1 Identify and evaluate alternative solution strategies to a given problem [A4, B2, C3]
8.2 Analyse, design and implement a computing-based solution to a structured problem [A2, B4, C1 C3, D5]
8.3 Design and implement well-documented, maintainable spreadsheets or programs suitable for data analysis tasks [C1].
8.4 Build models and carry out analyses of real-world problems using OR methodologies and spreadsheets [D3].
8.5 Perform custom calculations using Matlab and/or VBA or similar programming languages [A2, C1]
8.6 Write programs and/or develop spreadsheets to present and analyse quantitative data [D3].
9. The intended generic learning outcomes.
On successfully completing the module students will be able to:
9.1 Identify and analyse criteria and specifications appropriate to specific problems and plan strategies for their solution. [A4, B3]
9.2 Demonstrate a basic analytical ability and its relevance to everyday life. [B7]
9.3 Apply principles of effective information management, information organisation and information retrieval skills to information of various kinds. [C3]
9.4 Deploy effectively the tools used for the construction and documentation of software, with particular emphasis on understanding the whole process involved in using computers to solve practical problems. [B7, C4]
9.5 Demonstrate effective use of general IT facilities, manage one's own learning and development including time management and organisational skills. [D3, D4]
9.6 Analyse and draw reasoned conclusions concerning structured and, to a more limited extent, unstructured problems. [B9]
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