Posted: April 16th, 2022

Data Analytics for Business Problem Solving: Student Assignment Guide

Data Analytics and Management Assignment

Task Summary

Your organization is facing a problem. We’re all familiar with how unexpected challenges can pop up in any workplace setting. Your manager has decided that the problem can be solved by analysing a data set. You have been asked to do the data analysis and report your findings and recommendations.

It’s rewarding to dive into data and uncover insights that can really make a difference.

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For the dataset, choose one of two options.

1. Use a dataset from an organization you are involved with.
2. Use a data set provided for on iLearn.

An “organisation you are involved with” could be your employer but does not have to be.

You MUST use data that already exists. You MUST NOT create new data, because you don’t have ethical approval to collect new primary data.

Keeping ethics in mind helps build trust in your work.

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If you choose to use a data set from your organisation, you MUST have the consent of a manager in the organisation. You are expected to anonymise the provided data to remove all information that identifies the company.

Ifyouareusingadatasetfromyourorganisation,youmustconstructascenariowhich contains the problem, along with a question that can be answered by analysing the data set. You may ask colleagues, such as your manager, for ideas about a suitable scenario.

Collaborating with colleagues can spark great ideas for your scenario.

An example of a business problem is falling revenues from sales. The question could be: “Why are sales falling? The data set to analyse could be the last year of unit product sales for all product lines across all channels.

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If you are not currently with a suitable organisation, or you have no access to a data set which you can analyse, please use one of the datasets provided on iLearn.

The iLearn datasets are designed to give you a solid foundation for your analysis.

Assignment Tasks

Part 1

Summarise and characterize the problem that needs a solution through data analysis. Formulate a question that will be answered through analysis of the dataset. (LO2) (25 marks)

This part allows you to set the stage for your entire analysis.

Part 2

Critically analyse the methods that appear to have been used together the data. You may make assumptions. For example, you could assume that the dataset was created from a survey of customers, so you could critically analyse the survey design, execution, and sampling. (LO1) (25 marks)

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Thinking critically about data collection adds depth to your understanding.

Part 3

Execute appropriate data analytics activities to answer the question you created in Part1 ,and present your findings and recommendations based on your analysis. (LO3, LO4, LO5) (50 marks)

Here is where your technical skills really shine through.

Notes

Because of the nature of the assignment, your report may contain lots of numerical information. Such numbers do NOT count to the word quota. You may also put numerical data in appendices, which also do not count to the quota.

This flexibility lets you focus on the narrative without worrying too much about word limits.

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End of questions

Assignment Instructions

As part of the formal assessment for the programme you are required to submit a Data Analytics and Management assignment. Please refer to your Student Handbook for full details of the programme assessment scheme and general information on preparing and submitting assignments.

Taking the time to review the handbook can save you a lot of stress later on.

For this assignment, you must choose one dataset from the list of datasets on iLearn or use a dataset from an organisation you are involved with.

For this assignment, you must produce either: Option 1: A 3000 word written report, or

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Option 2: A15-minute narrated Power Point presentation. Option 1 – 3000 word Written Report Guidelines Maximum word count: 3000 words

Submission file type: MSWord Document or PDF file.

Choosing the format that suits your strengths can make the assignment more enjoyable.

Option2–15-minuteNarratedPowerPointSlidesGuidelines

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Your assignment should include: a title slide containing your student number, the module name, the submission deadline and the exact number of your submitted slides; the appendices if relevant; and a reference list in AU Harvard system(s).

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.

Anonymous marking ensures fairness for everyone.

You are asked to produce an audio narrated PowerPoint presentation that covers all the assignment tasks above and fulfils all the Learning Outcomes below. Please note that tutors will use the assessment criteria set out below in assessing your work.

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Recording time: 15 minutes

Maximum slides: 15 slides (Minimum slides–12 slides)

Submission file type: Audio narrated MS PowerPoint. Please do not submit audio or video format files.

Use a good combination of text, data, and visuals.

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Practicing your narration can help you stay within the time limit.

Narrate the slides – add audio recordings for each slide explaining the content of the slides in answer to the assignment tasks (At least 1 minute recording per slide).

The slide count excludes title and reference slides.

Learning Outcomes (LO)

By completing this assessment, you will have shown and be assessed on all five learning outcomes:

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1. LO1: Analyse methods of gathering data and their value related to a specific problem.
2. LO2: Assess a business domain problem.
3. LO3: Demonstrate appropriate analytical methods based on the dataset and identified problem to be addressed.
4. LO4: Make appropriate recommendations based on the findings of analytical activities
5. LO5: Discipline Expertise: knowledge and understanding of chosen field. Possess a range of skills to operate within this sector, have a keen awareness of current developments in working practice being well positioned to respond to change.

These outcomes are key to developing your expertise in data analytics.

You will be graded based on how well you meet these learning outcomes. Your marker will use a rubric to grade your work, and you can find this on the “My Assessment” tab on the module iLearn page. A copy is also provided below.

Familiarizing yourself with the rubric can guide your efforts effectively.

Guidelines and Policies

You can find links to more useful information about the assignment and university policies below.

How Do You Handle Group Projects?

We can write sections for group projects, ensuring consistency with your team’s style and integrating seamlessly with other parts. Whether you are working with three people or ten, we make sure the tone and structure stay unified. Our writers collaborate to match your group’s vision. Upload any shared guidelines to keep everything aligned. With our support, group assignments become smoother and more cohesive.

Exploring these resources will support your success in this course.

References

• Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276. https://doi.org/10.1016/j.jbusres.2018.12.044
• Sheng, J., Amankwah-Amoah, J., Khan, Z., & Wang, X. (2021). COVID-19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32(4), 1164-1183. https://doi.org/10.1111/1467-8551.12496
• Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599. https://doi.org/10.1016/j.ijpe.2019.107599
• Nam, D., Lee, J., & Lee, H. (2019). Business analytics use in CRM: A nomological net from IT competence to CRM performance. International Journal of Information Management, 45, 233-245. https://doi.org/10.1016/j.ijinfomgt.2018.01.005

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