Data Analytics for Apple Technology Company
Data Handling and Decision Making
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)
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:
1. Analyse methods of auditing data holdings and gap identification.
2. Critically analyse theoretical and core processes of data manipulation and mining.
3. Utilise and evaluate basic statistical concepts.
4. Appreciate ethical issues and their importance to data handling and decision making.
5. Develop a practical ability with data analysis and data mining methods to analyse and interpret data sets.
6. Make recommendations based upon the findings from data analysis.
7. 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: 5,000 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 the 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 100% of the marks for this module.
The aim of this assignment is twofold:
• Conduct and report on an audit of the data environment in an organisation of your choice.
• Based on the outcomes of the audit, develop a report presenting statistical analysis of a large data set coupled with a concluding narrative demonstrating appropriate recommendations.
NB1: Organisation name, other business or stakeholder particulars and any personally identifiable information can be fully anonymised.
NB2: If you are re-sitting this assessment for the first time, you may re-work your original submission if you wish. However, if this is your third attempt at this assessment, you must submit a piece of work which is substantially different from your first two attempts.
An audit of the data environment an organisation operates in represents a starting point in developing a big data analytics framework, and as such, is a key part of data enabled decision-making at all levels. This audit should include the following processes:
• Identification of major data sources and flows between the organisation and its stakeholders;
• Formulation of the improvement in data integrity as well as its storing, processing and reporting mechanisms;
• Recommendations on compliance procedures, specifically, data protection, privacy and ethical assurance.
A statistical analysis of a large data set is used to support and enhance the decision-making process in a case organisation. It should include the following components.
• Identification of a strategic decision which can be supported by data analytics;
• Description of relevant dataset;
• Selection and application of data mining procedures to the data set in question.
• Development of a report on forecasting and/or trends obtained through data mining, which should be visualised with tables, charts and diagrams.
• Recommendations based on the outcomes of the aforementioned analysis.
Analyse the key sources and flows of data that are collected, processed, stored and taken into account in the organisation’s decision-making process. Your analysis must include:
• A statement of all the current financial and non-financial data the organisation uses in its decision making;
• An analysis of data integrity and identified gaps;
• A map between business functions and data sources.
Based on the outcomes of your response to Question 1, propose how decision making in the organisation can be streamlined and better informed based on (a) additional analysis of the currently observed data, and (b) any extra types of data that can be retrieved, stored and processed for augmenting the currently available information. Your response must include:
• Description of data flows between the organisation and all of its stakeholders identified and ranked in terms of their relevance to strategic, tactical, and operational decision making;
• An approach to the improvement in data integrity of the proposed data analytics framework;
• Data protection and ethical assurance requirements.
(10 marks) (LO 1, 4) Task 3
Critically analyse a specific decision the organisation currently needs to make, and how big data can be expected to improve its quality. Your response must include:
• A statement of major strategic decisions and how the current financial and non- financial data are currently used to inform the respective decision making;
• Analysis of a particular strategic decision that needs to be made within the organisation based on data analytics, and the critical importance of this decision for the organisational competitive advantage or performance improvement;
• Identification of a specific data set to be analysed for supporting the stated decision. The assignment must utilise a large data set obtained from the chosen organisation.
(LO 1, 7)
Discuss the specific large dataset discussed in Task 3. Analyse this dataset in terms of:
• The business-related information the dataset represents;
• An explanation of how it has been obtained and what steps are taken for data filtering, cleaning and preparation;
• An analysis of how representative the data set is, and potential limitations associated with its application.
(10 marks) (LO 2, 7) Task 5
Discuss the data mining procedures performed using Excel, SPSS and / or Weka with the dataset in question that can be used to inform decision making. Include in your discussion:
• Statements of immediate observations of business performance using descriptive data analysis;
• An organisational forecast report based on inferential data analysis and / or application of machine learning techniques.
• Justification and critical evaluation of specific statistical models adopted in the data mining procedures.
(1500 words) (30 marks)
(LO 2, 3, 5)
Present a visualisation and interpretation of results as would be expected in business and academic reporting – this interpretation must be supported extensively by tables, charts and diagrams demonstrating how the outcomes of analysis performed in the previous task can be interpreted.
(20 marks) (LO 3, 5, 7)
Present recommendations for decision making based on the analysis described earlier as well as a proposal on the deployment of extra data sources potentially capable of further augmenting the organisational big data framework.
(10 marks) (LO 6, 7) Formative Feedback
You have the opportunity to submit an outline and up to 50% of each task 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.
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 on iLearn contains useful resources relating to referencing.
You must use the AU Harvard Referencing method in your assignment.
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.
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).
Assessment Criteria: Level 7
Level 7 is characterised by an expectation of students’ expertise in their specialism. Students are semi-autonomous, demonstrating independence in the negotiation of assessment tasks (including the major project) and the ability to evaluate, challenge, modify and develop theory and practice. Students are expected to demonstrate an ability to isolate and focus on the significant features of problems and to offer synthetic and coherent solutions, with some students producing original or innovative work in their specialism that is potentially worthy of publication by the University. A clear appreciation of ethical considerations (as appropriate) is also a prerequisite.
Grade Mark Bands Generic Assessment Criteria
Distinction 70%+ Excellent analysis of key issues and concepts/. Excellent development of conceptual structures and argument, making consistent use of scholarly conventions. Excellent research skills, independence of thought, an extremely high level of intellectual rigour and consistency, exceptional expressive / professional skills, and substantial creativity and originality. Excellent academic/intellectual skills. Work pushes the boundaries of the discipline and demonstrates an awareness of relevant ethical considerations. Work may be considered for publication by the university
Merit 60-69% Very good level of competence demonstrated. High level of theory application. Very good analysis of key issues and concepts. Development of conceptual structures and argument making consistent use of scholarly conventions. Some evidence of original thought and a general awareness of relevant ethical considerations
Pass 50-59% A satisfactory to good performance. Basic knowledge of key issues and concepts. Generally descriptive, with restricted analysis of existing scholarly material and little argument development. Use of scholarly conventions inconsistent. The work lacks original thought. Some awareness of relevant ethical considerations. Satisfactory professional skills (where appropriate).
Marginal Fail 40-49% Limited research skills impede use of learning resources and problem solving. Significant problems with structure/accuracy in expression. Very weak academic / intellectual / professional skills. Limited use of scholarly conventions. Errors in expression and the work may lack structure overall
39% and below A poor performance in which there are substantial gaps in knowledge and understanding, underpinning theory and ethical considerations. Little evidence of research skills, use of learning resources and problem solving. Major problems with structure/ accuracy in expression. Professional skills not present. Very weak academic / intellectual / professional skills. No evidence of use of scholarly conventions.
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