Data Handling and Decision Making
Part 2: Report
Date for Submission: Please refer to the timetable on ilearn
(The submission portal on iLearn will close at 14:00 UK time on the date of submission)
Assignment Brief
As part of the formal assessment for the programme you are required to submit a Data Handling and Decision Making report. Please refer to your Student Handbook for full details of the programme assessment scheme and general information on preparing and submitting assignments.
Learning Outcomes (LO):
After completing the module, you should be able to:
- Analyse methods of auditing data holdings and gap
- Critically analyse theoretical and core processes of data manipulation and
- Utilise and evaluate basic statistical
- Appreciate ethical issues and their importance to data handling and decision
- Develop a practical ability with data analysis and data mining methods to analyse and interpret data sets.
- Make recommendations based upon the findings from data
- Graduate Attribute – Effective Communication
Communicate effectively both, verbally and in writing, using a range of media widely used in relevant professional context.
Maximum word count: 4,000 words
Please note that exceeding the word count by over 10% will result in a reduction in grade by the same percentage that the word count is exceeded.
You must not include your name in your submission because Arden University operates anonymous marking, which means that markers should not be aware of the identity of the student. However, please do not forget to include your STU number.
Assignment Task – Report
This assignment is worth 80% of the marks for this module.
Assignment Part 2: Data-Driven Decision Support
Data-driven decision support encompasses a range of the most essential processes of data analytics, including data preparation and integration, modelling using statistical and/or machine learning techniques, and data presentation. The aim of this activity is to empower the organisational decision-making with statistically tested and systematically evaluated decision options. These options can be ranked using inferential models, such as forecasting, prediction and/or classification.
In Part 1 of this assignment, you have identified a case study – an organisation or project of your choice, as well as various data sources and datasets available to it, and an important organisational decision. In this part, you have an opportunity to demonstrate how this specific decision formulated in Part 1 in the context of your chosen case study can be supported with data analytics.
Task 2.1
Discuss data preparation process, including
Explanation of data collection, filtering and integration procedures.
- Analysis of data
- Statement on generalisability and limitations of the integrated
(800 words)
(20 marks)
(LO 2)
Task 2.2
Perform data modelling, which should specifically demonstrate:
- Selection and justification of the inferential and/or machine learning models, most relevant to the objectives of your case
- Application of statistical tools such as Excel, SPSS and/or Weka, to your model and reporting on the initial outcomes of your
- Explanation what the decision in question should be, based on these outcomes.
(2000 words)
(40 marks)
(LO 2, 3)
Task 2.3
Present further outcomes, in support of the decision obtained in Task 2.2. This discussion should be visualised with a range of charts and tables showing the identified and analysed relationships. It should also be accompanied with detailed interpretation of the demonstrated results.
(700 words)
(20 marks)
(LO 5, 7)
Task 2.4
Propose recommendations on the implementation, acceptance and assessment of the decision arrived at in Tasks 2.2 and 2.3, and discuss how this decision can contribute to strategic management of the chosen organisation or project.
(500 words)
(20 marks)
(LO 6, 7)
End of questions
Formative Feedback
You have the opportunity to submit a complete draft of this assignment to receive formative feedback.
The feedback is designed to help you develop areas of your work and it helps you develop your skills as an independent learner.
Your work must be submitted to your tutor at least two weeks prior to the assessment submission date. This is to allow time for you to reflect on the feedback and draft your final submission.
Formative feedback will not be given to work submitted after the above date.
Student Guidelines
Development of academic skills:
You MUST underpin your analysis and evaluation of the key issues with appropriate and wide ranging academic research and ensure this is referenced using the AU Harvard system. The My Study Skills Area contains the following useful resources:
Guide to Harvard Referencing http://moodle.bl.rdi.co.uk/guides/HarvardRef/AU_Harvard_Quick_Ref_Guide.pdf
Guide to Harvard Citation http://moodle.bl.rdi.co.uk/guides/HarvardRef/AU_Guide_to_Harvard_Citation.pdf
You must use the AU Harvard Referencing method in your assignment.
Additional notes:
Students are required to indicate the exact word count on the title page of the assessment.
The word count excludes the title page, tables, figures, diagrams, footnotes, reference list and appendices. Where assessment questions have been reprinted from the assessment brief these will also be excluded from the word count. ALL other printed words ARE included in the word count See ‘Word Count Policy’ on the homepage of this module for more information.
Submission Guidance
Assignments submitted late will not be accepted and will be marked as a 0% fail.
Your assessment should be submitted as a single Word (MS Word) or PDF file. For more information please see the “Guide to Submitting an Assignment” document available on the module page on iLearn.
You must ensure that the submitted assignment is all your own work and that all sources used are correctly attributed. Penalties apply to assignments which show evidence of academic unfair practice. (See the Student Handbook which is on the homepage of your module and also in the Induction Area).
