PREPARING A MASTERS DISSERTATION PORTFOLIO FOR A MASTERS IN BUSINESS ANALYTICS

2023-24

All students completing the Master’s in Business Analytics programme are required to undertake and pass a dissertation module, involving an independent analytics project. The portfolio dissertation is an opportunity for students to demonstrate their knowledge and understanding of business analytics gained during semester one and two. The portfolio dissertation involves carrying out an independent analytics project, drawing on the skills gained during the programme. This involves an initial project proposal and feasibility study, followed by more substantial technical and written components. Students have flexibility to choose their own datasets and topics, and guidance is provided on this during the initial dissertation training.

The portfolio dissertation is worth one third of the overall marks for the MSc Business Analytics. It is therefore important to focus diligently throughout the training and undertaking of the project, as this is a substantial and important part of the overall degree.

The portfolio dissertation will take place in semester 3. Any students with incomplete or failed modules, where there are concerns about their ability to progress to dissertation, will be called to a student progress meeting after the June exam board. At this meeting, the Director of Graduate Studies will discuss your options with you. 

The guidelines in this handbook explain what is required and provides general information on preparing and submitting your portfolio dissertation. Students will attend dissertation training after semester two work is completed, as well as workshops over the summer. 

Before submission of each element of the portfolio you should ensure that you have presented the document in the required format and have included all of the relevant sections.   

If there are any parts of this document which you do not understand, please raise this during the dissertation training, post on the canvas discussion forum, or email the programme director. 

University Regulations

Please note that all module requirements in this document are subject to the overriding  imprimatur of the University’s Regulations and Supplementary Regulations for taught  postgraduate programmes. It is the responsibility of the student to acquaint themselves  with these regulations.

1. Essential Requirements

The MSc Business Analytics has been developed to meet the demand for qualified professionals, who possess the necessary expertise to realise end-to-end business analytics solutions and are equipped to utilise data for business decision-making purposes. The final portfolio project, involving three elements, will involve the holistic application of business knowledge and technical and statistical skills to a defined business analytics project. The portfolio elements are designed to engage students in an independent and rigorous research project, whilst also ensuring they engage reflectively with professional and business practice. 

The dissertation portfolio allows students to apply the technical and theoretical knowledge from the course in the context of an analytics project. This will usually include the application of advanced analytics and machine learning, and can also include data storage, and data exploration and visualisation. The portfolio consists of three linked elements:

1. A project proposal / feasibility study of 2500 words +- 10%. This component is worth 25% of the overall dissertation mark.
2. A research report of 7500 words +- 10% detailing the research that was carried out. This element is worth 50% of the overall dissertation mark;
3. A technical report, logbook and reflective discussion (2500 words +- 10%), and appendices containing any code used (appendices are not included in the word count) This element is worth 25% of the overall dissertation mark;

The dissertation portfolio module forms one-third of the overall assessment for the degree (i.e. 60 of the 180 CATS points) and is thus a significant undertaking.   

Important submission dates:

• The deadline for final submission of the project proposal / feasibility study is 4pm Friday 28^th June. The project proposal must be submitted via CANVAS. Feedback on the proposal will be provided at the first supervision meeting. The completed ethics approval form (appendix 2) must also be submitted as an appendix to the proposal.

• The deadline for submission of the final portfolio is 4pm Friday 13th September, 2024, via Canvas. This will involve submission of the remaining two elements:

1. The research report
2. The technical report, logbook and reflective discussion 

The submission setup in Canvas means that your work will automatically be passed through Turnitin (see appendix 6). Please include the completed dissertation checklist as an appendix (see appendix 3).  

Elements submitted after the deadline will be subject to the normal University penalties for late submission. In addition, there may be significant fees implications for students who do not submit on time.  

Important Information on Grading

• The overall mark recorded for the module will be out of 100%
• Each element will be marked out of 100 drawing from the PGT Conceptual Equivalent Framework. The mark will then be adjusted to 25%, 50% and 25% respectively for each of the three elements, before being summed up for a total mark out of 100% for the entire 60 CAT module. 
• All three elements will be marked after the final submission deadline in September.
• Students must attain an aggregate mark of 50% or above to pass the module.
• Students must achieve an overall mark of 50% or higher to be considered for the award of Masters in Business Analytics.  

Dissertation Learning Outcomes 

On completion of the dissertation, students will be able to:  

• Undertake and manage a small business analytics project. 
• Develop a business analytics solution. 
• Critically evaluate the role of the solution in solving a specific problem, and in particular the strengths and limitations of the solution in solving the problem. 
• Evaluate the legal and ethical implications of the solution. 
• Synthesise, analyse, interpret, and critically evaluate information from a variety of different sources.  

Dissertation Training, Workshops and Supervision

Students will undertake ‘block’ training at the beginning of semester 3. This will provide guidance on the key written and technical components of the portfolio dissertation.

Students will attend workshops during the summer, which will cover key elements of the dissertation process, such as writing the literature review, analytics tasks, and preparing the final document for submission. 

Each student will be allocated with a supervisor early in the dissertation process. Students will have the opportunity to meet twice with their supervisor during the dissertation. At the first supervision meeting, the supervisor will provide feedback on the final submitted project proposal. The aim of this feedback is to provide advice and guidance for the student to consider whilst undertaking the full project. The supervisor can also read and provide feedback on one section of the research report (for example, the methodology or discussion section).

Please also remember that the dissertation is an independent piece of work. This is your opportunity to demonstrate your ability to conduct independent research and to write it up into a dissertation which presents a coherent, well organised analysis and evaluation to a clearly defined business analytics problem.   

Word Length 

The word count for the project proposal and feasibility study is 2500 words.

The word count for the research report is 7500 words.

The word count for the technical report, logbook, and reflective discussion is 2500 words.

The word count includes the title page, acknowledgements, contents, tables and footnotes, but excludes appendices and the reference list. The word count should be clearly stated on the front cover of each submission element.

It is permissible to exceed the word limit by 10% only. Once the word limit has been exceeded by more than 10%, a penalty of 10 marks deducted from the mark awarded will be imposed.  

Students who do not include a word count on the submission will normally be penalised by the deduction of 10 marks from the mark awarded. 

Extensions

Extensions for dissertation submissions will only be granted on the basis of extenuating circumstances, as outlined in the General Regulations: University Calendar for Postgraduate Students 2023-2024. The duration of any extension approved will be proportional to the impact of the extenuating circumstances, and up to a maximum of two weeks.

If you wish to be considered for an extension, you must complete the online form in line with the School’s PGT exceptional circumstances process and University Regulations and submit this for consideration well in advance of the submission deadline. No ‘informal’ extensions to dissertation submission dates will be given, neither will there be any retrospective approvals for extensions after the Dissertation submission deadline has passed.

Please submit an application for exceptional circumstances to be considered here: 

For further information on exceptional circumstances please visit http://www..ac.uk/directorates/sgc/ec/The final decision regarding a student’s application to have exceptional circumstances considered will be made by the Director of Graduate Studies.

If a student is granted an extension, regardless of the length of the extension or revised submission date, the School cannot guarantee the submission will be marked in time for the November Exam Boards (in preparation for December graduations) and their confirmation of final programme outcome may be delayed until the January Exam Board. This will mean that students who have extensions beyond the 13th September deadline may not be eligible to graduate until July 2025.

However, if your circumstances change and you think you may be unable to meet the dissertation deadline, please communicate this to your Programme Director to discuss the options for completing your programme and the implications for your graduation date. The School will always try to work with students to avoid any unnecessary delays.

Unless extensions are approved, dissertations submitted after the deadline will be subject to the normal University penalties for late submission and there may be fee implications if you do not adhere to this guidance

Non-submission of dissertations

Students who do not submit any elements of the portfolio by the set deadline and who do not have any approved exceptional circumstances will fall under the regulations for late submission of coursework.

If a student fails the dissertation portfolio module, as a result of late or non-submission and is permitted by the Board of Examiners to re-submit, he/she will be re-enrolled on a part-time basis and will be required to pay the appropriate module fee. Dissertations must be resubmitted by the deadline set by the School. This will be clearly communicated to the student. 

Failed dissertation portfolios

Students who fail the dissertation portfolio module at the first attempt may be permitted, by the Board of Examiners, to re-submit the failed elements. For any re-submission (where there are no exceptional circumstances) the maximum mark for the module will be capped at 50%. If a student does not wish to resubmit the dissertation, he/she may elect to graduate with a Postgraduate Diploma (subject to having successfully completed all taught modules). This would then be confirmed at the next Board of Examiners meeting.

Students who are permitted by the School to re-submit a failed dissertation will be re-enrolled on a part-time basis and will be required to pay the appropriate fee. Failed elements must be re-submitted by the deadline set by the School. This will be clearly communicated to the student.  

2. Preparing the Dissertation Portfolio

The MSc Business Analytics dissertation portfolio is an original and substantial piece of technical and written work. The dissertation portfolio provides students with the opportunity to undertake an independent project. This project will involve the development of a technical business analytics solution incorporating elements from the course. The suggested technologies for the solution will be those covered in the course. Solutions can include a combination of the analytics components included in the course such as machine learning, and visualisation, and data management and storage. It is recognised that, in some cases, projects may focus on specific components (e.g. predictive analytics, or text analytics), and this should be detailed at the proposal stage. Students will be required to obtain data as part of the project. This will involve the use of secondary data. Potential data sources will be discussed during the dissertation training. 

In addition to the technical solution, students will be required to produce a written report including a review of the literature, methodology for ‘solving’ the problem, results, discussion and conclusions. Alongside this, students will produce a technical report, log book, and reflective discussion.

The dissertation portfolio project requires students to draw from their learning from across the course, incorporating knowledge from the three core business analytics domains: statistics, computing, and business. 

3. Acceptable Topics

Deciding on your topic is a key issue. You need to consider questions such as: 

• What is the business problem or question? 
• Why is this issue relevant? 
• Is there data available to undertake the analytics? 
• How has analytics been applied to the problem in the past? 
• What has already been written about the topic? 
• Is there a gap in the existing body of knowledge? 
• Can the gap be filled by my project? 
• Is the research feasible within the restrictions of a Masters dissertation? 

Once you have identified a possible topic, you must consider the data you need. 

• What kind of data do you need? 
• How can you go about obtaining the data? 
• What difficulties are you likely to encounter? 

Some potential topics may have to be rejected because there is no data available. You need to identify such difficulties as early as possible. Feasibility of the project is vitally important and thus it is very important to submit your proposal on time and to discuss this with your supervisor.  

You should pay full attention to the feedback and advice provided at the proposal stage in assisting you in identifying and refining the project. 

4. Formulating your Topic

Arguably, identification and selection of your topic is the most difficult part of the whole dissertation process. It is also true that the perfect topic does not exist, so you will need to define a researchable topic and work on it, rather than wasting time trying to determine the unattainable perfect topic. However, a good dissertation usually has a clear and relatively narrow focus, an interesting, perhaps even novel, approach and is executed in a logical and informed manner. Therefore, it is worth expending effort on the preparatory and often uncomfortable processes of: 

• identifying a prime focus which interests you; 
• considering various alternative or complementary ways of tackling the issues; 
• developing your ideas and arguments in a comprehensible way (i.e. comprehensible to someone other than you). 

It is important that you put sufficient effort into this formulation stage.  

Some factors which you should take into account when determining what a reasonable topic is, include: - 

Timing

Your dissertation project must be completed within a pre-determined time limit. You can determine a work schedule if you work back from the stipulated submission date and consider tasks such as: typing up the report; writing various drafts; analysing the data; and conducting the literature review.

Data Sources 

There are a range of potential data sources which could be used for the dissertation project. It is useful to familiarise yourself with the available data when deciding on a topic. Examples of potential data sources include: 

• OpenDataNI: https://www.opendatani.gov.uk/ 
• UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php 
• Kaggle: www.kaggle.com 
• Twitter and other social media 
• Web scraped data 
• Company data via. the library: https://libguides..ac.uk/business/businessdatabases 
• UK data service: https://www.ukdataservice.ac.uk/ 
• NISRA: https://www.nisra.gov.uk/ 
• Data.gov.uk 

Care must be taken to ensure data is stored and processed appropriately, in line with legal, ethical, and organisational requirements. 

Please note: You must submit your completed ethics form via CANVAS along with the project proposal. Any major changes to the project after the proposal stage must be notified via email to your supervisor. Failure to submit a Research Ethics Approval Form or to update the information on the form, if circumstances change, will result in sanctions up to and including a fail mark for the dissertation. Primary data should not be collected (e.g. surveys/interviews should not be carried out) as this will require additional ethical scrutiny. If you believe there is an exceptional circumstance why it is crucial to collect primary data, this MUST be discussed with your supervisor in advance of any data collection. Failure to adhere to the ethics approval process is considered an academic offence and would be dealt with accordingly. 

You should refer to Appendix 2 for additional information. 

5. Common Pitfalls

Planning and completing any major project is not without its challenges. Listed below are some common problems which you need to be aware of and try to address. 

• You may attempt to research a topic that is too general and wide in scope which cannot be completed in sufficient depth within the time allowed, tools and techniques available, and within the word limit imposed. 

• You start analysing data or writing up before you think carefully about the business problem or before you have fully reviewed relevant literature.  You then either analyse (or in some cases collect) data which is insufficiently focused and possibly unusable or you have to rewrite an entire section. 

• You do not properly grasp or comprehend what is required before you start your independent research. To help you understand what is required, you should put a lot of effort into the proposal stage, and should take on board feedback given. 

• You do not plan ahead or keep to your plan. You should work through a series of self imposed targets which you discipline yourself to achieve. 

• You exceed the word limit and incur a penalty (you must present your work concisely).

• You submit work late and incur a late penalty (you must follow university regulations regarding late submission and extensions, all of which are detailed on the  web site and signposted within the Canvas QMS7000 module).

• In addition, you should also consider your technical strengths and weaknesses when deciding on a topic, as well as your personal interests, and your future career plans – these factors can help narrow the focus when thinking about a topic.   

6. Training and Workshops

The portfolio dissertation builds on the materials covered during the MSc Business Analytics programme. Additional training specific to the portfolio will be delivered at the beginning of semester three. This will take the form of block training, covering the structure and format of the portfolio, as well as specific technical sessions. 

During the summer months, students will have the opportunity to attend workshop sessions, which will focus on specific elements of the portfolio. The aim of these sessions is to provide guidance on the elements of the portfolio that students are working on at different points in the summer, as well as to address any general queries.

Students are encouraged to post any specific questions on the CANVAS discussion module. These will then be collated and discussed during the workshop sessions. Many students will face similar issues, so it is important to post these here.

There are also a number of drop-in clinics arranged throughout the summer. For specific issues relating to individual projects, it is important to attend the drop-in clinic where help can been provided by a member of staff. Please note that your supervisor will be taking annual leave during the summer and may not be available – therefore please use the scheduled workshops and drop-in clinics if seeking further support throughout the summer. 

Students should also be aware that the dissertation portfolio is an independent piece of selfdirected work. The aim is to provide the opportunity for students to demonstrate their ability to carry out an independent piece of work, which means that guidance will be limited to the two supervision meetings and the specific training sessions and workshops. A key element of working in business analytics is the ability to solve problems encountered, and in particular to overcome technical barriers. Students will have gained skills in this area during the programme, and are encouraged to employ and further develop this independence during the project. 

7. Planning, Timetable and Milestones

As part of the project proposal you should submit a project plan, including a Gantt chart detailing the key timeframes and milestones. This should include a series of milestones against which you can monitor your progress to help you stay on track. You should pay close attention to feedback around the timeframes, and adjust as necessary to ensure the project is feasible. The timetable or schedule of work you adopt will depend on your topic, how you intend to collect your information etc. Your success in managing the whole process will also depend on how well you plan the work and monitor progress against your plan.   

Many student problems concerning dissertation completion arise due to poor time management rather than a lack of ability to write up the elements required for the dissertation portfolio. 

8. Guidance on Methods and Technical Components

The choice of appropriate analytics tools and method(s) is central to the success of your dissertation. Common questions raised by students beginning to think about the dissertation include which data sources are most appropriate, and which analytics techniques are most appropriate. You can gain some insight into this by reviewing the sources suggested and through reading business analytics papers. You should include details of the data for the project in the project proposal and proposal presentation. The feasibility study aspect of the project proposal will also help ensure the data is fit for purpose.

The technical component of the project must include a machine learning or advanced analytics component. Projects should also include data exploration and visualisation. In some cases, it may also be appropriate to include a more advanced data storage solution. For example, a SQL database could be used to combine data from multiple sources prior to building machine learning models. In other cases, this step could be performed in the coding language, for example using dplyr in R, or using python. Students should clearly justify the technical design of their solution in the dissertation, including justification of the tools used. 

Usually the technical components should be selected from those covered in the course, and must include an element of coding. These should also be detailed in the project proposal.  

9. Structure and Content of the Project Proposal and Feasibility Study   

 The project proposal and feasibility study is a crucial element of the overall project, and is in itself worth 25% of the module mark. The aim of the proposal and feasibility study is to ensure that the project has been well designed, and is feasible. At this stage you will need to decide on the overall aims of the project, as well as what data will be used, and how this will be analysed. You will also need to carry out a small feasibility study, by undertaking a preliminary analysis of the data to ensure the data is suitable to meet the aims of the project.

When developing the proposal you will also need to consider any ethical implications. You must also complete and submit an ethics approval form with the project proposal. This should be included as an appendix, and as such is not included in the word count.

You will also have the opportunity to discuss the proposal with your supervisor who will provide guidance on feasibility and methodology. 

The following sections should be included in the project proposal submission:

Introduction and Aims: Introduce the project, and clearly specify the project aims and objectives and/or research questions.

Background: Provide a brief review of the background literature that is most important in informing the study. Here you should focus on summarising the five most important papers that inform your study, as a more detailed literature review is included in the research report.

Proposed Methodology: This section should summarise how you plan to undertake the project. This should include details of the data that will be used, the target (dependent) and predictor (independent) variables, the intended analytics methods, and consideration of any potential ethical issues.

Preliminary data analysis: To ensure the feasibility of the study you should carry out a preliminary analysis of the data. This should include a detailed assessment of the data quality, summary statistics and visualisations and correlation/association analysis. It is recognised that the exact content of the preliminary analysis could vary depending on the specific project.

Conclusion: In this section you should discuss the suitability of the data to meet the aims of the project, as well as any interesting preliminary findings.

Project Plan: The project plan should detail the next steps in the project. This should include key milestones for both the written components and technical components. Here you should plan out when you expect to have each chapter of the written submissions completed, as well as the key milestones for the technical components. This should include a Gantt chart. As part of the project plan, please also detail the key risks to the completion of the project. You should include the 2-3 most important risks that could compromise or delay the project. This may include issues around data, or technical components. Undertake and manage a small business analytics project.

Appendix 1: Completed ethical approval form (the form for completion can be found in appendix 2 of this handbook)

The final project proposal must be submitted via CANVAS by 4pm Friday 28^th June 2024. This will allow time for your supervisor to read the proposal prior to your first supervision meeting, which will take place in early July. Marks for the dissertation are normally released after all components have been submitted and evaluated. 

10. Structure and Content of the Technical report, logbook, and reflective discussion

This element of the portfolio focuses on the technical components of the solution including a technical report and logbook. In addition, this component also includes a reflective discussion. 

Technical Report: The technical report should detail the key elements of the solution. This section should not duplicate the methodology section of the research report. Rather you should focus more on the technical components, for example, why certain technical choices were made around choice of coding language and packages. What alternatives were considered when developing the technical solution. Are there any areas that could be improved, for example, to increase performance or accuracy. How does your technical solution differ from others presented in the literature. What detailed steps were taken to process the data and develop the solution?

Logbook: Keeping a log book is a useful way of documenting the steps that were taken during the development of a solution, and the choices that were made. For this logbook, you should include six entries, which should be spaced evenly over the course of the project. These should briefly summarise the work that was carried out in the preceding time period, as well as important technical decisions that were taken and why these choices were made. Based on the log book entries, it should be possible for the reader to understand the main tasks that were carried out during the project.

Reflective discussion: The reflective discussion should focus on your personal development as a business analytics professional through carrying out the project. You should consider the key skills that you have developed, and how these will benefit you in your future career. You should also consider which elements of the project you found challenging, and where you would make improvements.

Appendix 1: Code that was written to develop the solution. This should be included as text in the appendix of the submission (Rather than code files).

Appendix X: Further appendices can include screenshots of the solution, as well as other technical information you feel is appropriate. 

11. Research Report Structure

The research report should follow the structure of a typical research paper. The layout of your research report should typically follow this basic structure: - 

• Title page  
• Declaration  
• Acknowledgements (optional) 
• Abstract 
• Table of Contents 
• Introduction
• Background / Literature
• Methodology  
• Findings 
• Discussion of Findings (this is where you relate your findings back to the literature) 
• Conclusions 
• Recommendations 
• Reference list 
• Appendices. Please note that these should not duplicate what is included in the technical report.

Your dissertation must conform to the style requirements.  

Guidance on content: - Additional relevant details are provided in the appendices 

• Title Page:  you should use the prescribed format  

• Declaration: A signed (digital signature) declaration page certifying your work. 

• Acknowledgements: It is polite to acknowledge help given by others at the beginning of the report in an Acknowledgements section. This could include individuals and organisations that have supplied specific assistance, and should be limited to no more than a single page.  

• Abstract: An abstract describing the contents of the report. This must be short (probably in the region of about 300 words) providing details of the problem addressed, main arguments, brief details of the solution design, conclusions and recommendations of the report. It must be designed to be read independently of the rest of the report and references to the dissertation and other literature will not normally be included. 

• Table of Contents:  Your Table of Contents should include, as a minimum, a list of chapter numbers, chapter titles and page numbers for each chapter, together with details of any appendices you may be including. You may, if you wish, expand the detail given to provide information on sections within chapters, but you must not obscure the clarity of the Table of Contents with this additional material. 

You should also normally include a list of the figures and tables in your report (unless you have only a small number of tables and figures). 

• Main Text and Organisation: The actual ordering of sections will depend upon the topic and your approach, but will normally be as suggested, below:- 

1. Introduction - This section should explain the context within which the report is situated, as well as the business/research problem. You should include the research question, aims and objectives of the project, and the intended contribution to practice and to the wider body of knowledge of the topic. The section should include an outline of the report structure. 
2. Background – In this section you should provide a summary of the research and practitioner literature that is most relevant to the topic. For example, you should consider prior approaches to solving the problem, and relevant theory or research that is relevant to the topic. You should use relevant keywords to search online databases such as Google Scholar, Science Direct, IEEE Xplore, and Business Source Premier. Based on this literature, you should summarise the ‘state of the art’ in your chosen topic. 
3. Methodology – The methodology is of crucial importance to the success of a business analytics report, and, as such, this is a critical section in the report.  

You should, therefore, explain in detail the design of the project, the data used, and the methods, tools and techniques employed to store, process and analyse the data, so that the reader can evaluate the validity of your findings, conclusions and recommendations. Below is a possible format for this section- 

• Restate the problem / questions you are investigating and the project aims and objectives. 
• Discuss any analytics methodology used (e.g. CRISP-DM)  o Leading from the definition of the problem, explain the data that is to be analysed.  o Explain how the data was obtained 
• Detail the technical solution. Explain how the data was stored, processed and analysed. This should include details of any analytics techniques (e.g. machine learning algorithms used). This will be more of an overview compared with the technical report.
• Discuss the limitations of the project design and methods you employed. 

Findings - Describe the main results from the analysis. In addition to the written description, numerical results will usually also be presented in tabular format, either in the body of the report, or in a separate appendix. This section should include a description of the results of any descriptive statistics, visualisation, machine learning models, or outputs from other analyses carried out.

Discussion of Findings – Demonstrate the significance of your arguments or results and make appropriate linkages between your findings and the wider literature.

Conclusions– Briefly summarise what you have written from problem definition to results and discussion. Indicate the extent to which the problem has been addressed or the question has been answered and the research objectives met. Discuss how the investigation has contributed to theory and practice.  

Recommendations – Consider the limitations of your research and make suggestions for future investigations. In terms of recommendations, what action should be taken based on your findings, by whom and when? Your recommendations must be consistent with, and supported by, the evidence and arguments contained in your dissertation. Normally recommendations for both future research and practice should be made.  

Note:  Both your conclusions and recommendations should follow logically from the literature reviewed and the findings from undertaking the project. 

Although most students will follow the structure above, in a small number of cases it may be more appropriate to focus more on a particular industry setting, in which case an industry report is acceptable. This will follow the same structure as above, but will place more focus on industry application. If this is the case it should be agreed at the proposal stage. This option would only be used for example in the case of working with a specific company.

12. Assessment

The components of the portfolio are assessed on its academic merit and intellectual content for both the written and technical components. This includes the clarity of the aims and background, the choice and use of tools and methods, the robustness of the analysis and the discussion and interpretation of results. The presentation of the written work is also important. Good work is informative, thoughtful and well organized. Outstanding work is usually distinguished by great thoroughness, insight and/or originality and substantial depth of analysis. 

The portfolio is read and assessed and marked against the University’s postgraduate conceptual equivalents scale. This scale and full details of the assessment criteria can be found in Appendix 7. The actual mark sheet employed, which has been informed by the University’s conceptual equivalents scale PG, is also provided in the appendix.   

A rigorous process of moderation is undertaken internally (by appropriate academics from the ITAO Subject Group) and a range of portfolios are sent to the External Examiner for feedback. All marks are then discussed and finally confirmed at the appropriate Board of Examiners.    

13. Plagiarism

Plagiarism, Collusion and Fabrication

Plagiarism, collusion and the fabrication of information are unprofessional and unacceptable forms of behaviour, which the University regards as serious academic offences.  

Definitions and procedures for dealing with such academic offences can be found in the University’s General Regulations on the University’s website (includes a list of academic offences and definitions):

Plagiarism 

Plagiarism is defined as the presentation of the work of others as the writer’s own without appropriate acknowledgement. This includes auto-plagiarism (to use excerpts from his or her previous work without appropriate acknowledgement) and self-plagiarism (to submit one piece of work more than once, e.g. where it has been previously submitted for a different assignment). Remember: your dissertation will be graded as a new piece of work and therefore must be significantly dissimilar from previous pieces of work.  

It is expected that students write their own code to carry out analytics tasks. The source of any code which you did not write yourself must also be clearly acknowledged.   

Each student’s dissertation is passed through ‘Turnitin’ software linked through from Canvas, as this will help to avoid plagiarism. Please refer to Appendix 6 for details.  

Collusion  

It is a serious academic offence for two or more students to work together on an assignment that is meant to be done individually and hand the work in as if they had each worked independently. It is expected that the work being assessed, unless specifically designated as a group assessment, shall be the sole work of the named student.

Fabrication 

It is a serious academic offence for a student to claim to have carried out any form of research which he/she has not in fact carried out, or to invent or falsify data, evidence, or experimental results. It is also an academic offence for a student knowingly to make use of falsified data as described above. 

Contract Cheating

It is a serious offence for a student to commission or seek to commission (either paid or unpaid) another individual or artificial intelligence software tool to complete academic work on their behalf. You will be expected to sign a student declaration confirming the portfolio submission is the student’s own work.  

Suspected plagiarism, fabrication or collusion will be dealt with thus:  

A member of staff who discovers possible plagiarism, fabrication or collusion in work submitted for assessment shall report the suspected offence in writing to the Head of the School (or nominee) where the student is registered. The Head of School (or nominee) shall arrange for the alleged offence to be investigated and penalties applied, as appropriate. In the most severe cases, this could result in a student being required to withdraw from the programme with no degree being awarded. 

A member of staff who discovers possible plagiarism, fabrication or collusion in work that does not count towards the assessment of the course, or in drafts of work, shall normally deal with this informally. This will involve advising the student of the academic conventions with regard to referencing, reporting of results, etc., applying in the discipline. 

Students’ Responsibility

It is the responsibility of each student to familiarise himself or herself with the conventions of academic writing. Students are required to cite all works used and to adopt Harvard referencing in their work (see Appendix 8). In the event of plagiarism being identified in a student’s work, the School will take action against that student and any claim of ignorance of the required academic conventions will not be accepted.  

14. Confidentiality

Even though you anonymise your sources of information and/or the organisations involved in your research, you and/or they may have concerns about confidentiality, especially where such sources and/or organisations are difficult to disguise. Where this applies and it is of concern, you may ask the Programme Director and the Board of Examiners to have your dissertation embargoed. This means it will not be made available for consultation by anyone other than those involved in examining the dissertation. 

15. A Final Word

Do not panic!  Although the portfolio dissertation is a major piece of work, you do not have to be a superhuman to complete it successfully.  However, unlike an essay or report, it cannot be completed in a quick burst of effort over a few days. The best strategy is to plan effectively and work diligently on the project throughout the time available, taking advantage of presentation feedback and workshops.  

16. Useful References to Get Started 

A number of books provide relevant practical advice on aspects of the dissertation process, and specifically on analytics. Some examples are below. 

Books focusing on analytics: 

Baumer, B. S. and Kaplan, D. T. (2016) Modern data science with R, Chapman and Hall 

Hastie, T. and Tibshirani (2014) An Introduction to Statistical Learning, New York, Springer 

Hastie, T., Tibshirani, R. and Friedman, J. (2009) The elements of statistical learning: data mining, inference, and prediction, New York, Springer Science and Business Media

Kelleher, J.D., Mac Namee, B.  and D’Arcy, A. (2015), Fundamentals of Machine Learning for Predictive Data Analytics, The MIT Press: USA 

Linoff, G.S. and Berry, M.J.A. (2011), Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3^rd Edition, Wiley: USA 

Wickham, H. (2017) ‘R for Data Science’ Available online: https://r4ds.had.co.nz/ 

Wickham, H. (2017) Advanced R, Available online: https://adv-r.hadley.nz 

APPENDICES  

Appendix 1- Style Requirements for the Dissertation General 

1. Proofing:  Use a spell checker. A surprising number of dissertations contain unnecessary spelling and typing mistakes.  Responsibility for proofing rests with the student.
2. Font size: Dissertations should written in 11 or 12 point type, using a clear font such as Times New Roman. 
3. Margins: Margins should be not less than 40mm at the binding edge (left margin) and not less than 20mm at all other margins.  
4. Spacing: The text should be double-spaced; quotations should be indented within the text and single spaced. Footnotes or endnotes should be single-spaced. 
5. Numbering: Pages should be numbered consecutively throughout the dissertation.   
6. Title page:  The title page must give the full title of the dissertation, the name of the author, the degree for which it is being submitted and the School concerned, word count and the year of submission.   
7. Declaration:  A signed declaration must be included after the title page, but before the acknowledgements/abstract.   
8. Table of Contents: A table of contents, indicating the chapters and sections within chapters with page numbers, must be included. Appendices should be numbered consecutively. 
9. Tables & Figures: A list of tables, figures and illustrations should be included where these are numerous. 
10. Abstract: An abstract of approximately 300 words stating the purpose, sources, methods, results, conclusions and recommendations, should be included following the title page and list of contents. 
11. Reference List: A full reference list must be included at the end before the appendices (see below). 
12. Appendices: Full details of the technical components of the project must be included in the appendices, including any code used and screenshots of any other components. Please include the dissertation checklist as an appendix. 

ANY OMISSIONS OR ERRORS IN THE DISSERTATION ARE YOUR RESPONSIBILITY

Style 

1. Dates: ‘16 January 2012’ not ‘January 16, 2012’. 
2. Numbers: numbers under 10 should be spelled out in letters, except where the number is attached to a unit of quantity (e.g. 1 mm, or 3 kg) or percentages (e.g. 3 per cent).  Use commas in numbers with four or more digits: 1,000. Undertake and manage a small business analytics project.
3. Quotation marks: use single quotation marks, reserving double ones for quotes within quotes, e.g. ‘The noun “guerrilla” is variously spelt’.  If the quotation forms a complete sentence it should have its closing full stop inside the closing quotation mark; otherwise, the full stop should be outside the closing quotation mark.  For points of omission within a quotation use three spaced dots, with a space either side of the set. 

Quotations extending over four or more lines of text should be indented, with single spacing and no quotation marks. 

Abbreviations. Explain all but the most familiar abbreviations used on the first mention in the text, i.e. use the expanded version followed by the abbreviation in brackets: Total Quality Management (TQM), Human Resource Management (HRM). 

Per cent is usually spelt out in the text (15 per cent) but the symbol % may be used in notes and tables, and in the text itself if it occurs very frequently. 

1. Tables, Figures and other Illustrations: 

There should be separate sequences of Tables, Figures and Illustrations, e.g. Table 1, Table 2, etc.; Figure 1, Figure 2, etc; Illustration 1, Illustration 2, etc. 

Each table, figure and illustration should be numbered and have a clear title, with a note of the source.  For example: 

Table 1. Number of Mergers & Acquisitions in Australian Investment Banks 1995-2005   

[Table] 

(Source: Macquarie (2006), p. 6.) 

This would be a numerical table.  Be sure Macquarie is included in your reference list. 

Figure 1.  Incidence of customer call-centre complaints 1998-2003 

[graph, bar chart, map, or other form of graphic representation] 

(Source: PriceWaterhouseCoopers, annual survey.) 

Locate tables, figures and illustrations as closely as possible to the relevant text, unless they are so bulky that they require a separate appendix.  The text should call attention to the table, figure or illustration, e.g. (Table 1) or (Figure 2). 

1. Footnotes:  Footnotes should be numbered in a continuous sequence for each chapter and the number should appear both in the text in superscript and either at the bottom of the page or at the end of the text.  Either position is acceptable, though footnotes at the bottom of the page are usually preferable from the reader’s point of view. 

Footnotes have three main uses: 

• to cite the authority for statements in the text (specific facts or opinions as well as exact quotations).  They enable a reader to find out exactly where you acquired your information; they should show the exact source of your information; 
• to make cross-references; 
• to comment upon, to amplify, or to qualify textual discussion – to provide a place for material which the writer thinks worthwhile to include but which would disrupt the flow  of argument if introduced into the text.  Footnotes should be kept to a minimum; if the information is important it should be in the text.  If not, it probably can be left out. 

1. Writing Style:  The dissertation must be written in plain, succinct and grammatical

English, avoiding jargon if possible.  Write a dissertation which may be understood easily by someone of tertiary education level who is not an expert on the subject(s) to which the topic relates. 

(i) Try to avoid using many words where a few will convey the same meaning.  For example (a real one), ‘The present writer undertook the task of analyzing the cases thus prepared, and came up with a set of tentative generalisations’ can be reduced to ‘The author analyzed the cases and produced a set of tentative generalisations’. 

(ii) Try not to be long-winded, flowery, or sensational.  Never use a longer word instead of a shorter one with the same meaning. It looks as if you are trying (and failing) to impress. ‘Within’ is no more ‘scientific’ than ‘in’, ‘most’ is far less pompous than ‘the majority of’,’ and why write ‘in order to’ when ‘to’ is what you mean? Avoid buzzwords, jargon or other fashionable terms. Be clear and straightforward. If you are searching for the right words, say to yourself, ‘what am I really trying to say?’ and write the answer down immediately! If you find yourself using the words ‘the fact that’ you will normally be waffling, e.g. ‘due to the fact that’ means ‘because’. Sensational and inflated language is unacceptable, meaning that words like huge, massive, enormous, vast, decimated and so on should almost never be used. Write like a scientist, and researcher not like a journalist!  

(iii) Do not be prescriptive, i.e., avoid phrases like ‘companies must …’.  Explaining your views about alternative policies and courses of action, using your evidence and ideas, is fine and very desirable, but it is not our task to tell others what to do. 

(iv) Blatantly ageist or sexist language should be avoided. Do not use words that stereotype people in terms of appearance, maturity, mental and physical competence and so on. Traditionally, masculine words have been used generically, for example mankind, but it is now usual and expected to use less sexist terms such as s/he, chairperson, human resource planning, and so on. 

(v) Avoid both very lengthy and very short paragraphs.  Five to seven sentences per paragraph is a good rule. Paragraph breaks should follow the logic of the discourse and be apparent to someone listening to the dissertation being read. The same structure may be used for each chapter except the first and final ones.  There should be a brief introductory section/paragraph and at the end of the chapter there should be a brief summary or concluding section/paragraph. 

(vi) Avoid the use of lists.  Instead, use good flowing English prose in developing your arguments.  The use of lists, including bullet points, looks and may be, lazy. 

(vii) Avoid use of bold, italics, inverted commas or underlining whenever you can.  Emphasis should normally come from your careful choice of specific words and your skilful phrasing, not from such generic artificial aids. 

Reference List 

References should be cited using the Harvard citation system. Start thinking about your reference list when you begin your research as it is the basis of your reference system.  Use of Refworks or a similar computer package can be helpful in managing your references and compiling your reference list.  Norma Menabney, the Subject Librarian for Management, can give your specific advice on the use of Refworks if you have not previously used it. 

Your dissertation must include a full reference list at the end (before the appendices), to show the sources you have used.  Sources should be listed in alphabetical order.  Where a number of publications by the same author are cited, these should be ordered by date of publication  e.g. a Harrison reference dated 2007 would come before a Harrison reference dated 2009.  Where a source has more than one author the first named author is followed by the second and subsequent authors.  

i) Book:  

Daft, R.L., Murphy, J. and Willmott, H. (2010) Organization Theory and Design, Andover UK:  Cengage Learning EMEA. 

ii) Edited book:  

Brush, C., Carter, N., Gatewood, E., Greene, P. and Hart, M. (Eds) (2006) Growth Oriented Women Entrepreneurs and Their Businesses: A Global Research Perspective, Cheltenham UK:  Edward Elgar Publishing. 

iii) Chapter in Book: 

Leitch, C.M., Hill, F. and Harrison, R.T. (2006) “The Growth and Financing of Women- Led Ventures: The Northern Ireland Experience.”  In C. Brush, N. Carter, E. Gatewood,

P. Green and M. Hart (Eds). (2006) Growth Oriented Women Entrepreneurs and Their Businesses: A Global Research Perspective, Cheltenham UK: Edward Elgar  Publishing. 

iv) Journal Articles: 

Molm, L.D, Collett, J.L. and Schaefer, D.R. (2007) “Building Solidarity through Generalized Exchange:  A Theory of Reciprocity”, American Journal of Sociology, 113, 1, pp. 205-242. 

If you have concerns about referencing or plagiarism, you can contact staff in The Graduate School. 

Appendices 

Appendices should include screenshots of technical solutions, any code that was written. 

Bulky material, such as screenshots, code or a lengthy series of tables, should be placed in appendices at the end of the dissertation. Each appendix should be numbered and given a concise title.  Make sure a cross-reference to the appendix is included in the body of the dissertation whenever the material from the appendix is being referred to, in order to alert the reader that it is available for consultation. Please include a copy of the dissertation checklist. 

You should not use appendices to pad out your dissertation with non-essential or unrelated material, nor should you include as an appendix material properly located in the body of the dissertation.  

Appendix 2 - Research Ethics Approval 

All students must obtain ethics approval for the dissertation portfolio. The completed ethics form must be submitted via CANVAS along with the proposal and feasibility study. Any major changes following the proposal, such as a change of dataset should be notified to the programme director. This will be reviewed by the students supervisor. Students will be contacted if there are any potential issues. Students whose ethics forms are approved will not be contacted individually. 

For the majority of Masters students gaining ethical approval is very straightforward and there are no overriding concerns about the data that is used. However, you need to ensure that you have given due care and attention as to how you will access data, how you will store this data (confidentiality, security etc. are primary considerations), and how, after your dissertation has been awarded a final confirmed mark, you will dispose of the data. 

Before completing the Ethical Approval Form, you should familiarise yourself with the University’s Policies on research and ethical approval, and, in particular, the following two documents: 

Policy and Principles on the Ethical Approval of Research   Code of Conduct and Integrity in Research Information and all appropriate documentation relating to the Ethical Approval Form are available on the Management School’s Student Sharepoint site   

Student Ethical Approval Form

Name of student…………………………………………………………………………….  

Student e-mail ………………………………………………..........................................  

Dissertation title…………………………………………………………………………….  

Section One – Overview of the Research Methodology. In this section you are required to provide an overview of your proposed research methodology. Areas which should be discussed include:  

Data Sources:  

ü  A description of the data set (s) that will be used in the project (what is included in the data, does it relate to identifiable individuals / organisations, how have you ensured the authenticity and quality of the data).

ü  Information about the source of the data (whether it is publicly available, who collected it, are there any usage restrictions).

ü  Are there any special ethical issues with the proposed data (e.g. does it include children, vulnerable adults, or people with special communication needs)

Method 

ü A description of how the research will be carried out and the procedures that you are intending to use (e.g. data visualisation, predictive analytics, text mining). 

Section Two: Checklist of ethical issues 

For each of the questions below, please select Yes or No, as appropriate.  

Please confirm that the project uses secondary data only (any deviation from this requires prior approval from the programme director along with additional ethics approval paperwork):

Yes / No

Does the study require access to data which is not publicly available? 

Yes / No

Will the study require the co-operation of a gatekeeper for initial access to the data?

(E.g. businesses or other organisations)  Yes / No  

Does the project involve data in which individuals or organisations can be identified? Yes / No

Does the study involve data relating to participants who are particularly vulnerable or unable to give informed consent? (e.g. Children, people with learning disabilities, or staff/student records)  Yes / No 

Will it be necessary for participants to take part in the study without their knowledge and consent at the time? (E.g. covert observation of people in non-public places)  Yes / No 

Will the study involve data relating to sensitive topics (e.g. sexual activity, drug use)? Yes / No  

If you have answered yes to any of the questions, or if there are other ethical issues or considerations, please explain these below: 

(input text here)

Section Three: Management of routine ethical issues 

Describe the ways in which the research design has addressed any ethical issues identified in section two (Refer to data security and privacy, informed consent, handling of sensitive data/information).  

(input text here)

If there are no ethical issues, provide a full explanation as to why this is the case (e.g. non-identifiable secondary data only is being used and does not include sensitive information). 

    (input text here)

How has your research design taken into account issues of risk (for example the possibility of being unable to gain access to data or data loss or disclosure of sensitive information)?

     

(input text here)

Section Four: Student declaration 

I have read and agree to abide by the requirements of the following University documents 

• Policy and Principles on the Ethical Approval of Research http://www..ac.uk/home/media/Media,599765,en.pdf   

• Code of Conduct and Integrity in Research http://www..ac.uk/home/media/Media,599772,en.pdf  

Student’s signature ……………………………………………… Date ………………………….. 

NB: If, as your study progresses, you are proposing to make changes to the data used or other ethical concerns arise, you must bring these to the attention of your supervisor.

Students will be informed at the proposal stage if there are any ethical concerns which should be addressed.         

Appendix 3 Dissertation Checklist Sheet 

A digitally signed copy of this form should be included as an appendix to the dissertation.  

Name:                                                                                                   

  

Date Submitted:                                                                                  

  

Signature (Digital):                                                                                          

 

I confirm that my dissertation contains the following prescribed elements:  

 

□	My dissertation portfolio meets the style requirements set out in the MSc Business Analytics Portfolio Dissertation Handbook including a word count on the front page of each element.
□	I have reviewed the Turnitin similarity report prior to submission.
□	My dissertation title captures succinctly the focus of my dissertation
□	My title page is formatted as prescribed in the MSc Business Analytics Portfolio Dissertation Handbook
□	The abstract provides a clear and succinct overview of my study
□	Each element contains a Table of Contents, and List of Figures and Tables (where appropriate)
□	My dissertation contains a statement of acknowledgement (optional)
□	The Introduction section of the research report, at a minimum, covers each of the following issues:
	-  Background to/context of the project
	-  Research question(s), aim(s) and objectives
	-  Why the project is necessary/important
	-    A summary of the Methodology  -    Outline of the key findings   -    Overview of chapter structure of the remainder of dissertation
□	The Background section of the research report, at a minimum, covers each of the following issues:   -    Synthesises the key technical literature relating to the topic -    Synthesises the key theoretical literature relating to the topic

□ The methodology section of the research report:  

- Details the procedures adopted in carrying out the project (e.g. the data source/acquisition, data processing, procedures for maximising rigour and robustness, methods of data analysis etc). Contains ethical considerations and decisions. This section should not duplicate the technical report, which focuses more on the detailed technical choices and steps. 

□ The findings section of the research report reports the results in detail and provides possible explanations for the various findings 

□ The discussion section of the research report makes appropriate linkages between the findings and the literature reviewed 

□ The conclusions section of the research report includes:  

- Conclusions about each research question and/or hypothesis  

- General conclusions about the research problem 

- Implications for theory, for policy and/or management practice  - Limitations of the research 

	-  Suggestions for practice and future research
□	The technical report, log book, and reflective discussion have each been included.
□	The reference list is in alphabetical order and follows the Harvard system
□	I have signed and dated the Candidate Declaration