2022/23 Taught Postgraduate Module Catalogue
OLUB5210M Business Analytics
15 creditsClass Size: 200
Taught: 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June View Timetable
Year running 2022/23
Pre-requisite qualificationsStudents are required to meet the programme entry requirements prior to studying the module.
This module is not approved as an Elective
Module summaryData is an invaluable resource in the 21st century, but in order to gain the full benefit from the information available, managers need to be able to transform data into insights through data analytics, particularly in situations where various conflicting sensibilities and stakeholders are involved. In this module, students will obtain an understanding of the processes that underlie the transformation of data into insights. Through studying the module students will: - Learn how to perform analysis on data. - Be able to interpret the result obtained when applying different analytical techniques. - Have the ability to make decisions based on the observed outcomes. - Be able to select an appropriate analytical method(s) when faced with a business and/or sustainability challenge carrying out any subsequent analysis in an appropriate and well-informed manner. - Have the ability to outline and implement select techniques closely related to the management of environmental assets and other related fields such as transportation (e.g. choice modelling and optimisation). No prior knowledge of business analytics and mathematical techniques will be required. The module will focus on interpreting results rather than a deep understanding of the algorithm and/or techniques upon which the calculations are based.
ObjectivesThe objective of this module is to provide students with an insight into business analytics and an ability to apply key elements of the analytical process (i.e. data extraction, transformation, analysis, and communication) with a special emphasis on common areas of application in Sustainability.
Students will gain theoretical knowledge, technical aptitude, and also further their analytical thinking with an ability to transform quantitative and qualitative data into insights which support informed decision making.
Upon completion of this module students will be able to:
1, Summarise what business analytics is and critically evaluate its potential benefits, limitations and application for a variety of organisations facing a range of sustainability issues.
2. Recognise the overall process of data analysis (for example CRISP-DM) and the appropriate analytical techniques for addressing different sustainability problems with the ability to determine the most appropriate approach depending on the question at hand and the analytical techniques respective limitations.
3. Prepare data and apply select data analytic methods (CBA, MCDA, indicators, risk analysis, regression, classification, forecasting, choice modelling, optimisation, and simulation).
4. Evaluate the performance of different analytic techniques and select the main outcome of interest for each technique.
5. Interpret results from the analysis and make informed recommendations based on findings.
6. Communicate analytical results at an appropriate level using suitable terminology and visualisation.
1. Introduction to business analytics and its application in the area of sustainability.
2. The process for data analysis from data extraction, transformation, analysis, and visualisation.
3. Identify appropriate analytical tools and techniques to address sustainability problems.
4. The implementation and interpretation of selected calculations/statistical techniques (e.g. regression, classification analysis, forecasting time-series).
5. The examination and application of selected analytical techniques related specifically to sustainability. (e.g. choice modelling, process optimisation and simulation.)
6. Appropriate ways to convey the outcomes of data analysis to stakeholders in order to aid organisational decision making.
|Independent online learning hours
|Private study hours
|Total Contact hours
|Total hours (100hr per 10 credits)
Private studyIndependent online learning refers to non-facilitated directed learning. Students will work through bespoke interactive learning resources and reflective activities in the VLE.
Private study refers to directed reading and self-directed research in support of learning activities and discussions, as well as in preparation for assessments.
In practical terms, students will gain theoretical knowledge through a variety of means including videos, and interactive learning content. Further to this, students will be able to practice using the analytical tools and frameworks through a series of formative exercises (with subject matter expert feedback provided) and collectively via the regular webinars and discussion forums.
Opportunities for Formative Feedback- The module’s digital learning materials provide regular opportunities for participants to check their understanding and gain feedback (e.g., case studies with short answer questions and automated feedback, MCQs with detailed feedback on correct/incorrect answers).
- Student will have the opportunity to learn the basics of Excel and R through the inclusion of formative exercises where students are required to implement/use either Excel or R (with feedback provided).
- The individual unit webinars and discussion forums provide opportunities for formative feedback from peers and tutors.
- The module assessments (7-minutes max pre-recorded presentation and 2000-word assignment) will enable student progress to be monitored.
- Student will be told of the assessment at the start of the module and will be provided with opportunities to ask questions to teaching staff before starting on the assignment.
Methods of assessment
|% of formal assessment
|5-7 minutes presentation
|Total percentage (Assessment Coursework)
Assessment 1: 30% (5-7 minutes pre-recorded presentation) (submitted in week 5 drawing on the content of units 1-3) Task: Students are given a sustainability problem. Drawing on the data provided students will develop an analytic plan to resolve the issue. The plan is likely to include: 1. a statement of the sustainability business problem; 2. a description of the specific variable in dataset that is of interest; 3. a description of the analytical method(s) that are suitable to help resolve the problem identified; 4. a summary of the results and a reflection on how these results could be utilized to make recommendations to resolve the sustainability problem. Students will be required to submit a transcript (or detailed notes) related to their presentation via Turnitin. Assessment 2: 70% (2000 words) (submitted in week 8 drawing on the content of units 4-6) Task: Students are given a sustainability problem and have access to a set of data. Drawing on this, the students are required to conduct an analysis using the data provided and a suitable methodology. Students are also asked to describe the results and provide recommendations. It is anticipated that students will provide: 1. a description of the dataset using graphics; 2. a summary of the results; 3. recommendations to resolve the sustainability business problem based on the results of the data analysis. Assessment one will enable students to demonstrate their knowledge of the analytical process. Assessment two will provide students with the opportunity to apply relevant analytical techniques and communicate the findings of this analysis and make recommendations. Resit will be by the failed element. In each case new a new data set will be provided.
Reading listThe reading list is available from the Library website
Last updated: 09/05/2022 16:35:36
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