代做QBUS6600 Data Analytics for Business Capstone Semester 1, 2024 Assignment 1代做留学生Python程序

日期：2024-03-17 04:42

QBUS6600

Data Analytics for Business Capstone

Semester 1, 2024

Assignment 1 (individual assignment)

1.  Key information

Required submissions:

.    Written report (in pdf, due date: Monday, March 25 by the end of the day).

.     Confidentiality Deep Poll online form (deadline for submission: March 11).

Submission instructions for the report will be posted on Canvas in Week 5.

Weight: 30% of your final grade.

Length: Your written report should have a maximum of 12 pages (single spaced, 11pt). Cover page, references, and appendix (if any) will not count towards the page limit.    Please keep in mind that  making  good  use  of your  audience’s time  is  an  essential  business  skill:  every sentence, table or figure should serve a purpose.

2.  Problem description

Please start by reading through the Project Outline document for your industry project, which you can find on the “Learn about our industry projects page” in the Week 1 module on Canvas. Focus on the Problem Description section of the Project Outline, especially the first and the third  bullet  points  (EDA and  Strategy), which are the  most  relevant  bullet  points  for Assignment  1.  Both your  analysis and your  recommendations  should  be  in  line with  the requirements/suggestions provided in the Project Outline.

As  a  business  analyst,  you  will  conduct  Exploratory  Data  Analysis  (EDA)  of  the  data corresponding to your industry project. You should aim to find or reveal all relevant properties, characteristics,  patterns,  and  statistics  hidden  in  the  data,  supporting  your  findings  with insightful plots and relevant statistical output.

Use  the  results  from  your  EDA  to  outline  a  preliminary  strategy  or  provide  preliminary recommendations to the management team corresponding to your selected industry project. You will have a chance to refine these recommendations in Assignment 2. Please refrain from extensive modelling and model selection – you will do them in Assignment 2. However, feel free to fit simple models (e.g., linear regression or logistic regression) for the purposes of EDA and understanding the relationships among the variables in the dataset.

3.  Written report

The purpose of the report is to describe, explain, and justify your findings to the management team corresponding to your selected industry project. You may assume that team members have training in business analytics, however, they are not experts in statistics or machine learning. The team’s time is important: please be concise and objective.

Suggested outline for the main parts of the report (further details below):

1.   Problem formulation.

2.   Data processing.

3.   Exploratory Data Analysis (EDA).

4.   Conclusions and preliminary recommendations.

You should consider breaking down the longer parts into smaller sections.

4.  Marking Scheme

Business context and problem formulation.	5%
Data processing.	30%
Exploratory Data Analysis (EDA).	45%
Conclusions and preliminary recommendations.	10%
Writing and presentation of the report.	10%
Total	100%

5.  Rubric (basic requirements)

Business context and problem formulation. Your report gives a detailed description of the problem that is being investigated, providing the context and background for the analysis.

Data processing. You describe the data  processing steps clearly and  in sufficient detail, justifying and explaining your choices and decisions. You handle missing values and other data issues appropriately.  You describe and explain your data transformations and/or your feature engineering process (if any). Your choices and decisions are justified by data analysis, domain knowledge, logic, and trial and error (if necessary).

Exploratory data analysis (EDA).  Your report provides a comprehensive description of your EDA  process,  presenting  selected  results.    Your   analysis   is  sufficiently  rich,  and  your visualizations are insightful. You study key variables and relationships among them using appropriate plots and descriptive statistics. You note any features of the data that may be relevant for model building in Assignment 2. You note the presence of outliers and any other anomalies that can affect the analysis. You explain the relevance of the EDA results to the underlying business problem and your subsequent recommendations. You clearly describe and justify the methods in your analysis. The choice of methods is logically related to the substantive problem, underlying theoretical knowledge, and data analysis. You interpret the statistical outputs that you provide.  You report crucial assumptions and whether they are potentially violated.

Conclusions and recommendations. The reasoning from the analysis and results to your conclusions   and   recommendations   is   logical   and   convincing.   Your   conclusions   and recommendations are written in plain language appropriate for non-technical audience.

Writing. Your writing is concise, clear, precise, and free of grammatical and spelling errors. You use appropriate technical terminology. Your paragraphs and sentences follow a clear logic and are well connected. If you use an abbreviation or label, you define it first.

Report layout. Your report is well organised and professionally presented, as if it had been prepared for a client later in your career. There are clear divisions between sections and paragraphs.

Tables. Your tables are appropriately formatted and have a clear layout. The tables have informative row and column labels. The tables are relatively easy to understand on their own. The tables do not contain information which is irrelevant to the discussion in your report. The tables are placed near the relevant discussion in your report. There is no text around your tables, and your tables are not images.

Figures (plots). Your figures are easy to understand and have informative titles, captions, labels, and legends. The figures are well formatted and laid out. The figures are placed near the relevant discussion in your report. Your figures have appropriate definition and quality. There is no text around your figures, and your figures are not screenshots.

Numbers. All numerical results are reported to suitable precision (typically no more than three decimal places, in some cases fewer).

Referencing.  You follow the University of Sydney referencing rules and guidelines.

Python code. The text of your report should be entirely free of Python code.

Note: you are strongly encouraged to use Python for all the steps of your data analysis. While there is  no  Python code submission for Assignment  1, you should  keep your code well- organized,  so  that  you  can  easily  extend/modify/reuse  this  code  for  the   purposes  of Assignment 2 (which will have a Python code submission requirement).

6.  Deductions

Marks may be deducted from each item in the marking scheme in the following cases:

.    The report is disorganised and/or has a poor layout.

.    There is an excess of abbreviations or labels that the reader may be unfamiliar with.

.    The report has an excessive number of grammatical or spelling mistakes.

.    The tables are difficult to read, for example, due to poor layout or labelling.

.    The figures are difficult to read, for example, due to poor layout or labelling.

.     Numbers are not appropriately rounded.

7.  Late Submission of the report

Late submissions are subject to a deduction of 5% of the maximum mark for each calendar day after the due date. After ten calendar days late, a mark of zero will be awarded.

8.  Late submission of the Confidentiality Deed Poll online form

It is a requirement of our QBUS6600 unit that all students complete the Confidentiality Deed Poll online form before gaining access to the datasets for the industry projects. The datasets are highly confidential, and you have responsibility to keep them secure and only use them for your QBUS6600 coursework. Submission of the Confidentiality Deed Poll online form. after the  March  11  deadline  is  subject  to  a  penalty  of  20%  for  Assignment  1.  Furthermore, assignments without a submission of the online form. will not be marked.