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N1550 Data Analytics for Accounting & Finance

Assessment Period:  August 2022 (A3)

Assessment Instrument Group Project (assessment type PRJ)

Note for A3 resit students: if you have previously submitted this assessment, then you should choose a different dataset from the one you have chosen before.

In contrast to the A1 exam period, you do not need to ask prior permission from the module convenor to choose a dataset.

Whilst you may carry out the assessment in a pair of two, we are aware that due to the resit conditions, many students will not be able to find a student partner. You may therefore submit the assessment individually without incurring a penalty. You do not need to raise the issue with the module convenor before you submit.

Your Assessment at a glance:

The aim of this assessment is to analyse a dataset of your choice using the techniques covered in the module.

Number of group members

One or Two

Number of words

2,000 +/- 10% as per Sussex policy.

 

Word count includes tables and charts that are part of the main body (i.e, not part of any optional appendices)

 

Word count excludes optional references and appendices.

 

Please supply tables and charts inline (not at the end).

 

References are optional in this assignment (apart from a reference to the dataset), if you include them please use Harvard referencing style.

Percentage of total mark

40%

Deadline

Please check Sussex Direct for the definite date and time.

Choice of dataset

You can choose a dataset of your choice, which must meet the following criteria:

1. It must be a public domain, freely available dataset.

2. The dataset must contain a metric variable which can realistically serve as a dependent variable (for example, a performance score of some kind)

3. The dataset must contain another metric variable which can realistically serve as an independent variable.

4. The dataset must contain at least one categorical variable (to assist with analysis). If you wish to go the extra mile, you could create a categorical variable from a metric variable using Python.

5. The dataset must contain at least 500 datapoints (double check with module convenor if you are very keen on a dataset which meets all other criteria, just not this one).

A good place to look for suitable datasets is Kaggle (https//www.kaggle.com) but this is not required.

Any report with a dataset that does not meet the above criteria will normally be capped at 40%.

Marking criteria

We will assess your report on the basis of the standard criteria for projects at the Year 2 Undergraduate Level, which you can find on Canvas.

More specific marking guidance for this project is provided in the section “Structure of the Report” below.

Structure of report

Use the following structure to write your report:

 

Mark weighting

Minimum required

(Mark guidance 40%-60%)

Going the extra mile

(Mark guidance 60%-80+%)

1. Introduction

15%

Introduce the dataset, and potential talking points you wish to investigate

 

Include equal contribution statement (see below).

 

Introduce the dataset, and potential talking points you wish to investigate

 

Include equal contribution statement (see below).

 

2. Mastering the Data

25%

Produce a database model for the dataset, either ERD or UML.

 

Identify primary key(s).

 

The model contains only one table (no one-to-many relationships needed)

 

Use Excel to retrieve the data.

Produce a database model for the dataset, either ERD or UML.

 

There are multiple tables for the dataset, and one-to-many relationships are clearly identified.

 

Identify primary and foreign keys.

 

Use a database to import the data. Join the datasets with SQL and export the final dataset to Excel.

3. Performing the tests

25%

Perform a regression analysis

 

Document the outcome.

Perform a regression analysis.

 

Use Python to import the dataset and highlight some unusual values.

 

Document the outcome.

4. Visualising the results

25%

Identify three “talking points” about your dataset, and use three appropriate visualisations to illustrate your talking points.

 

Provide a clear and concise narrative.

Identify three “talking points” about your dataset, and use three appropriate visualisations to illustrate your talking points.

 

Use non-traditional charts to illustrate your points (e.g., no pie charts, bar charts, or line charts).

 

5. Summary

10%

Wrap up your report

Wrap up your report

6. Optional References

 

 

 

7. Optional Appendices

 

 

 

For a definition of some of the terms, please refer to the module lectures, seminars, and textbook.

Learning Outcomes being Assessed

The following two course learning outcomes are being assessed with this instrument:

· LO2 Work effectively independently and collaboratively

· LO4 Communicate information, ideas, problems, and solutions to specialist and non-specialist audiences using a variety of technologies

The following two module learning outcomes are being assessed with this instrument:

· LO2 Develop and correctly interpret core data management concepts that are fundamental to the design of modern information systems in accounting and finance

· LO3 Extract, visualise, and communicate key trends and insights from large datasets in the context of accounting and finance