| Subject Code: | FINM4100 |
|---|---|
| Subject Name: | Analytics in Accounting and Finance |
| Assessment Title: | Wrangling & Exploring Data – Casey's Carwash |
| Assessment Type: | Data Wrangling |
| Weighting: | 40% |
| Total Marks: | 60 |
| Submission: | Moodle |
| Due Date: | Week 10 – Tuesday 19:55 AEST |
• You are required to wrangle data provided for a business. This will involve cleaning, structuring and standardising data in an Excel worksheet.
• You will then be asked to draw on your updated dataset data to answer a series of questions
• You are then required to upload your completed Excel worksheet for grading
Learning Outcomes: LO1 and LO3
You will be provided with an Excel workbook file containing numerous worksheets which contain a raw records table, customer master record, data rules and the questions you are required to answer. A separate worksheet is provided for you to provide responses to the questions asked.
You may use Generative AI to assist in the development of your assessment as per the guidance provided for Level 2 assessment which is outlined on page 5. With respect to the use of GenAI for this assessment:
You CAN:
You CAN NOT:
Directly copying and pasting responses without critical engagement, proper citation, or using GenAI against assessment instructions, will be penalised.
Students must submit their Excel workbook via Moodle on Tuesday of Week 10 at 19:55 AEST.
This file must be submitted as an Excel workbook to avoid any technical issues that may occur from incorrect file format upload. Uploaded files with a virus will not be considered legitimate submissions. Moodle will notify you if there is any issue with the submitted file. In this case, you must contact your facilitator via email and provide a brief description of the issue and a screen shot of the Moodle error message.
Students are encouraged to submit their work well in advance of the deadline to avoid any possible delay with Moodle submissions or any other technical difficulties.
https://www.kbs.edu.au/admissions/forms-and-policies
KBS values academic integrity. All students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct Policy.
Please read the policy to learn the answers to these questions:
• What is academic integrity and misconduct?
• What are the penalties for academic misconduct?
• How can I appeal my grade?
https://www.kbs.edu.au/admissions/forms-and-policies
Penalties may be applied for assessment submissions that exceed prescribed limits.
Students may seek study assistance from their local Academic Learning Advisor or refer to the resources on the MyKBS Academic Success Centre page. Further details can be accessed at https://elearning.kbs.edu.au/course/view.php?id=1481
Please see the level of Generative AI that this assessment has been designed to accept:
| Traffic Light | Amount of Generative Artificial Intelligence (Generative AI) usage | Evidence Required | This assessment (✓) |
|---|---|---|---|
| Level 1<br>Prohibited:<br>No Generative AI allowed | This assessment showcases your individual knowledge, skills and/or personal experiences in the absence of Generative AI support. | The use of generative AI is prohibited for this assessment and may potentially result in penalties for academic misconduct, including but not limited to a mark of zero for the assessment. | |
| Level 2<br>Optional:<br>You may use Generative AI for research and content generation that is appropriately referenced.<br>See assessment instructions for details | This assessment allows you to engage with Generative AI as a means of expanding your understanding, creativity, and idea generation in the research phase of your assessment and to produce content that enhances your assessment. I.e., images. You do not have to use it. | The use of GenAI is optional for this assessment.<br><br>Your collaboration with Generative AI must be clearly referenced just as you would reference any other resource type used. Click on the link below to learn how to reference Generative AI.<br>https://library.kaplan.edu.au/referencing-other-sources/referencing-other-sources-generative-ai<br><br>In addition, you must include an appendix that documents your Generative AI collaboration including all prompts and responses used for the assessment.<br><br>Unapproved use of generative AI as per assessment details during the content generation parts of your assessment may potentially result in penalties for academic misconduct, including but not limited to a mark of zero for the assessment. Ensure you follow the specific assessment instructions in the section above. | ✓ |
| Level 3<br>Compulsory:<br>You must use Generative AI to complete your assessment<br>See assessment instruction for details | This assessment fully integrates Generative AI, allowing you to harness the technology's full potential in collaboration with your own expertise.<br><br>Always check your assessment instructions carefully as there may still be limitations on what constitutes acceptable use, and these may be specific to each assessment. | You will be taught how to use generative AI and assessed on its use.<br><br>Your collaboration with Generative AI must be clearly referenced just as you would reference any other resource type used. Click on the link below to learn how to reference Generative AI.<br>https://library.kaplan.edu.au/referencing-other-sources/referencing-other-sources-generative-ai<br><br>In addition, you must include an appendix that documents your Generative AI collaboration including all prompts and responses used for the assessment.<br><br>Unapproved use of generative AI as per assessment details during the content generation parts of your assessment may potentially result in penalties for academic misconduct, including but not limited to a mark of zero for the assessment. Ensure you follow the specific assessment instructions in the section above. |
| Details | Mark | Failure<br>F<br>0-49 | Marginal<br>P<br>50-64 | Adequate<br>C<br>65-74 | Good<br>D<br>75-84 | Excellent<br>HD<br>85-100 |
|---|---|---|---|---|---|---|
| Data Wrangling – Perform Data validation, ensuring presentation and ease of understanding | 10 | Data validations are incomplete. Data presentation is unclear and difficult to follow. | Data validation has some validations. Data presentation is adequately clear and can be followed. | Adequate data validation. Data presentation is adequately clear and can be followed | Good data validation. Data presentation is mostly clear and easy to follow | Comprehensive data validation and data presentation is clear, concise and easy to follow |
| Data Wrangling – Validate the data for uniqueness and action accordingly | 8 | Data checks for uniqueness are not present, and actions have not been correctly applied. | Data checks for unique attributes have been performed with most actions being correctly applied. | Adequate data checks for unique attributes and most actions have been correctly applied. | Good data checks for unique attributes and correct actions have been applied. | Comprehensive and efficient data checks for unique attributes and correct actions have been applied. |
| Data Wrangling – Validate data accuracy and completeness and action accordingly | 13 | Data validation is limited. Fields are mostly inconsistent, inaccurate and incomplete. | Data validation has occurred with some data correctly updated to ensure consistent, accurate and complete fields. | Adequate data validation with most data correctly updated to ensure consistent, accurate and complete fields. | Good data validation with data correctly updated to ensure consistent, accurate and complete fields. | Comprehensive and efficient data validation with data correctly updated to ensure consistent, accurate and complete fields. |
| Data Wrangling – Structure, join, and enhance the data where needed. | 9 | Data structuring logic to enhance data, including data joins is unclear and incomplete. Formulas cannot be validated. | Some data structuring logic to enhance data, including data joins. Formulas are mostly present for inspection. | Adequate data structuring logic to enhance data, including data joins. All formulas are present for inspection. | Comprehensive data structuring logic to enhance data, including data joins. All formulas are present for inspection. | Comprehensive and efficient data structuring logic to enhance data, including data joins. All formulas are present for inspection. |
| Details | Mark | Failure<br>F<br>0-49 | Marginal<br>P<br>50-64 | Adequate<br>C<br>65-74 | Good<br>D<br>75-84 | Excellent<br>HD<br>85-100 |
|---|---|---|---|---|---|---|
| Data Exploration – Explore the data to answer the Customer Master questions | 5 | Minimal data exploration, resulting in most questions being answered inaccurately | Marginal data exploration, resulting in some questions being answered with an accurate outcome. | Adequate data exploration, resulting in many questions being answered with an accurate outcome. | Good data exploration, resulting in most questions being answered with an accurate outcome. | Comprehensive data exploration, resulting in all questions being answered with an accurate outcome. |
| Data Exploration – Explore the data to answer the Raw Records questions | 15 | Minimal data exploration, resulting in most questions being answered inaccurately | Marginal data exploration, resulting in some questions being answered with an accurate outcome. | Adequate data exploration, resulting in many questions being answered with an accurate outcome. | Good data exploration, resulting in most questions being answered with an accurate outcome. | Comprehensive data exploration, resulting in all questions being answered with an accurate outcome. |
Kaplan Business School
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