Business Intelligence and Data Analytics BI3005
- Subject Code :
BI3005
- Using appropriate machine learning algorithms and conventional statistical methods, write a report on the followings.
-
- 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?
B. Extract insights from the data. Discuss the implications of your findings within a business decision context. Position yourself as a data analyst advising a management team.
2. Write a 1000-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.
- Introduction (including methodology).
- Results, and discussion.
- References
Your report is intended for managerial level decision makers. They need actionable results. Include persuasive data visualisation where necessary????.
View Rubric
|
Business intelligence (qualitative) |
||
|
Business intelligence (qualitative) |
||
|
Criteria |
Ratings |
Points |
|
Introduction and methodology |
10 to >8 pts High distinction Shows a deep and insightful understanding of the data analytics. Include 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. Demonstrate evidence of a strong understanding in the data analytics (e.g., providing reasons for the choice of the estimators, and data cleaning). 8 to >7 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. 7 to >6 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. 6 to >5 pts Pass Shows a functional understanding of the data analytics. Analysis is mainly descriptive and have some inconsistencies. 5 to >0 pts Fail Shows little to no OR incorrect understanding of statistical analysis. |
/ 10 pts |
|
Result and discussion |
10 to >8 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. 8 to >7 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. 7 to >6 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. 6 to >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. 5 to >0 pts Fail Shows little to no OR incorrect understanding of statistical analysis. Provides descriptive levels of analysis. |
/ 10 pts |
|
Total points: 0 |
||
