代做N1550 Data Analytics for Accounting & Finance代写Python编程
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Assessment Instrument Group Project (assessment type PRJ)
Your Assessment at a glance.
Note for A3 resit students: In the A1 exam period, this PRJ had to be submitted as a group of two. In the A3 resit period, you should submit this assessment individually. You will not incur a penalty for solo submissions, and you do not need to raise the issue with the module convenor before you submit.
If you have previously submitted this assessment in A1 or before, 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.
The aim of this assessment is to analyse a dataset of your choice using the techniques covered in the module.
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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). Screenshot all Python code. References are optional in this assignment (apart from a reference to the dataset), if you include them please use Harvard referencing style. |
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Percentage of total mark |
40% |
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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 should ideally contain at least two tables connected by primary keys and foreign keys. If the dataset contains just one table, it should be clear that it has been denormalised.
3. The dataset must contain a metric variable which can realistically serve as a
dependent variable (for example, a performance score of some kind)
4. The dataset must contain another metric variable which can realistically serve as an
independent variable.
5. The dataset must contain at least one categorical variable (to assist with analysis). You could create a categorical variable from a metric variable using Python.
A good place to look for suitable datasets is Kaggle (https//www.kaggle.com) but this is not required. The textbook has a list of suitable sites in Chapter 2, Exhibit 2-1, p. 55.
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:
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IMPACT Step |
Mark weighting |
Minimum required (Mark guidance 40%- 60%) |
Going the extra mile (Mark guidance 60%- 80+%) |
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1. Identifying the questions |
15% |
Introduce the dataset, and three potential questions you wish to investigate Include equal contribution statement (see below). |
Introduce the dataset, and three potential questions you wish to investigate Include equal contribution statement (see below). |
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2. Mastering the Data |
25% |
Produce a database model for the dataset, either ERD or UML. Identify primary and foreign keys. (The model may contain only one table, but you can and should still identify how the table was constructed from normalised tables) Use Excel VLOOKUP or DB Browser for SQLite to access and join the data into a denormalised table. |
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 DB Browser for SQLite or Python to import the data. Join the datasets with Pandas and export the final dataset to Excel. |
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3. Performing test plan |
25% |
Perform. a regression analysis using Excel Document the outcome. The regression result may relate to your questions. |
Perform. a regression analysis using Excel or Python. Use Python to import the dataset and highlight some unusual values. Document the outcome. The regression result should relate to your questions. |
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4. Address and Refine Results |
25% |
Answer the three questions about your dataset, and use three appropriate visualisations to illustrate your answers. Provide a clear and concise narrative. |
Answer the three questions about your dataset, and use three appropriate visualisations to illustrate your answers. Include traditional & non-traditional charts to illustrate your points (something else other than pie charts, bar charts, or line charts). |
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5. Communicate Insights |
10% |
Wrap up your report. Write in plain English what you have found. |
Wrap up your report.
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6. Optional References |
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7. Optional Appendices |
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For a definition of some of the terms, please refer to the module lectures, seminars, and textbook.
Document all Python code that is used. A statement such as ‘we used Python’ is not sufficient. Liberally use screenshots to document your points.
All screenshots should be full-screen screenshots. We do not accept partial or strategically cropped screenshots.
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 nonspecialist 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