Business Intelligence and Data Analytics BUSN6505
- Subject Code :
BUSN6505
- Once you have decided on a project, download the appropriate dataset from their respective repositories. Also, take a look at this recording to get some ideas on how to approach this assignment. This is a high level overview.
- Go to Science Direct collection at ECU Library. You will need your ECU access credential to log into the database. Here is the link to the repository.
https://www-sciencedirect-com.ezproxy.ecu.edu.au/
- Search for research articles related to your project. You will use some of them as a foundation for your own analysis. Use the following questions to guide your endeavour. These questions will assist you to make your analysis relevant.
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- What are the findings?
- Where applicable, what are the variables, including the control variables, they use and why?
- What are the limitations of these existing studies?
- How would your analysis extend the existing research?
- Using appropriate machine learning algorithms and conventional statistical methods, write a report on the followings.
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- The estimators you use in the data analytics. This is the core of your discussion.
- Which estimators you use for the analysis? E.g., neural network, logistic regression, k-nearest neighbour.
- Explain why you choose them. What are their strengths and limitations?
- How well each estimator performs such as their accuracy?
- Discuss any limitations in the data, how these affect the estimators' performance, and how you address them.
- Extract inisghts from the data. Discuss the implications of your findings within a business decision context. Position yourself as an advisor for a group of investors. See the examples below.
- Bankruptcy prediction: Which metrics (financial ratios) are important and why they are relevant to your client's investment decision.
- Sentiment analysis: How strong the correlation between the sentiment and the variables of interest? What would be your advice for your clients?
- Write a 1500-word report on your analysis. The professional report is to be presented to an intelligent, non-specialist audience. You can use these headings to structure your report.
- Results, insights, discussion, and recommendation.
- Limitations and conclusion.
Your report is intended for managerial level decision makers. They dont need standardised beta andp-values. They need actionable results. Include persuasive data visualisation where necessary????.
View Rubric
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Business intelligence (quantitative) (1) |
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Business intelligence (quantitative) (1) |
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Criteria |
Ratings |
Points |
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Introduction |
5 to >4 pts High distinction The introduction is comprehensive, succinct and compelling with seamless links between the main elements. The introduction of the issues of the project attempts to address and a summary of the findings is systematically, analytically and insightfully provided. 4 to >3.5 pts Distinction Provides a clear and appropriate introduction to the report. Most of the elements of an introduction are covered (information on the scope, key findings, purpose and an overview of the report structure). 3.5 to >3 pts Credit The introduction is thorough and contains all required elements of an introduction. The introduction links the main elements well. 3 to >2.5 pts Pass A superficial introduction with some key points are missing. 2.5 to >0 pts Fail The introduction does not clearly relate to the topic and/or does not introduce the report adequately. |
/ 5 pts |
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Methodology |
15 to >12 pts High distinction Shows a deep and insightful understanding of the data analytics. An acknowledgement of the data structure such as missing values and the outliers is presence, and appropriate remedies are taken. Multiple estimators are used correctly. Evidence of a strong understanding in the data analytics is demonstrated such as reasons for the choice of the estimators, and data cleaning. 12 to >10.5 pts Distinction Shows a strong understanding of the statistical analysis. Multiple estimators are used correctly. Strengths of the estimators are discussed with sufficient but limited depth. An acknowledgement of the data structure such as missing values and the outliers is presence. 10.5 to >9 pts Credit Demonstrates a functional understanding of the data analytics. Limited number of estimators are used. Limited or insufficient evidence of the data preprocessing tasks. Limited number of estimators are used in the analysis. 9 to >7.5 pts Pass Shows a functional understanding of the data analytics. Analysis is mainly descriptive and have some inconsistencies. 7.5 to >0 pts Fail Shows little to no OR incorrect understanding of statistical analysis. |
/ 15 pts |
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Results, discussion, and recommendations |
15 to >12 pts High distinction Shows a deep and insightful understanding of the data analytics and other key metrics relevant to the choice of both conventional statistical methods and the machine learning algorithms. The discussion and recommendations coherent and substantive. 12 to >10.5 pts Distinction Shows a strong understanding of the data analytics and other key metrics relevant to the choice of both conventional statistical methods and the machine learning algorithms. The discussion and recommendations are coherent with a good depth of analysis of the result. 10.5 to >9 pts Credit Shows working understanding of the data analytics and other key metrics relevant to the choice of both conventional statistical methods and the machine learning algorithms. The discussion and recommendations are coherent. 9 to >7.5 pts Pass Shows functional understanding of the data analytics. Limited discussions on the key metrics relevant to the choice of the conventional statistical methods or the machine learning algorithms. Provides descriptive levels of analyses, although richer than the lower grade bound. 7.5 to >0 pts Fail Shows little to no, OR incorrect use data analytics. Discussions on the key metrics relevant to the choice of the conventional statistical methods or the machine learning algorithms. Provides descriptive levels of analysis. |
/ 15 pts |
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Limitations and conclusion |
10 to >8 pts High distinction The conclusion and limitation are systematically, analytically and insightfully communicated. 8 to >7 pts Distinction A strong conclusion is provided, covering all key points and mirroring the introduction. The limitations are acknowledged and substantively discussed. 7 to >6 pts Credit The conclusion is provided covering the key points and limitations are acknowledged. 6 to >5 pts Pass Not convincing in its summary of the report and some key points are missing. 5 to >0 pts Fail The conclusion and limitations are inadequate OR are not provided. |
/ 10 pts |
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Contextual language and reference view longer description |
5 to >4 pts High distinction Virtually free of mechanical errors. Vocabulary is sophisticated and business, academic and/or disciplinary terminology is used to convey nuanced and enhanced meaning. Writing has a sophisticated structure and highly logical order. Sophisticated and seamless transitional expression and other signposts enhance readability as well as advanced use of headings and subheadings, which organise content to guide the reader through the whole report. The writing is fluent, sophisticated and skilfully communicates meaning. 4 to >3.5 pts Distinction Writing has minimal mechanical errors. Writes with a varied vocabulary where business, academic and/or disciplinary terminology conveys meaning with fluency. Writing has a well-balanced structure and cohesive and logical order. Effective use of smooth transitional expressions and other signposts support readability as well as well-developed use of headings and subheadings, which organise content to guide the reader through the report. Meaning is conveyed cohesively and concisely. 3.5 to >3 pts Credit Some mechanical errors are still evident. Writes with a wide vocabulary. Business, academic and/or disciplinary terminology is used consistently and correctly most of the time. Writing has a clear structure and logical order, with consistent use of transitions to link sections. Acceptable use of headings and subheadings, which in part organise content to guide the reader. The meaning is clear. 3 to >2.5 pts Pass Inconsistent use of mechanical elements of writing. Vocabulary is basic. Business, academic and/or disciplinary terminology is used correctly and contextually most of the time. The writing structure is basic but logical; however, there is inconsistent linkage of ideas, concepts and sections and the transitional connections are not always clear. Use of headings and subheadings to distinguish main structural elements but needs improvement to guide the reader. The meaning is not always clear. 2.5 to >0 pts No marks Numerous mechanical errors. Vocabulary is limited and there is incorrect use of discipline terminology or it is used out of context. Writing is not balanced, the structure is not logical and there is no linkage between ideas, concepts and sections. The report is very difficult to read and/or fails to follow the structure outlined. Paragraphs are unfocused, incoherent or lack transition of thoughts. Headings are not used. The written communication issues significantly interfere with the meaning. |
/ 5 pts |
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Total points: 0 |
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