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Assignment/Coursework Remit

Assignment

The second assignment is an individual report that should meet the following expectations:

1. Define a business problem (challenge/issue) based on your interest. This problem (challenge/issue) should have the potential to be studied, investigated, and analysed by data. The importance of the problem/topic should be discussed and elaborated. The idea of the business challenge could emerge from news, social media, science, books, articles, your personal interest, experience, working experience, etc. The considered area to study is not limited and could cover any business or industry (manufacturing, service, sport, etc.) at any level (local, regional, national, global, etc.). Determine the research questions and research objectives of your report. The research questions/objectives should try to employ business analytics methods, including (i) clustering algorithms, (ii) classification algorithms, (iii) predictive models, etc. Try to limit the research questions/objectives to between 3 and 5. Extract the features (variables, attributes, criteria, etc.) required to respond to the research questions or achieve the objectives. These features could be based on the current literature or the studied case specifications. Hence, a brief literature review is required to extract the features/factors/variables from reputable and scientific databases. Moreover, review the data, data sets, data gathered, data analysis framework, etc. Implement the pre-processing stage, including (i) data cleaning, (ii) data integration, (iii) data reduction, (iv) data transformation, and (v) data discretisation. Finalise the data set by any possible software package, including SPSS, Minitab, Python, etc. 

2. The data analysis process/framework should be discussed in detail. Each report should illustrate the data analysis framework that will be applied. The data analysis framework is based on the research problem, questions and objectives and is preferred to cover (i) data pre-processing (e.g., data cleaning, data validation, data reduction, proximity analysis, etc.), (ii) data processing (e.g., clustering, classification, statistical analysis, association rules, predictive modelling, etc.). The selected methods in the data analysis stage should respond to the research questions. Hence, the research questions must be linked to the data analysis methods. Select and Implement clustering and classification algorithms on the finalised data set. Clustering methods to be used include (i) K-Means, (ii) K-medoids or PAM, (iii) Hierarchical clustering, (iv) Two-stage clustering, (v) Density-Based clustering, and (vi) OPTICS clustering. Classification methods to be used include (i) Decision Tree, (ii) CART, (iii) Nearest Neighbour, (iv) Naïve Based, and (v) Support vector machines. At least two clustering algorithms and two classification algorithms should be selected and implemented. Remark that clustering and classification validity should be measured and discussed. Any possible software package, including SPSS, Minitab, Python, etc., is allowed. For the finalised data set, and according to the report objectives/questions, design an initial predictive model based on statistical modelling approaches. The statistical models include (i) linear regression, (ii) non-linear regression, (iii) logistic regression, (iv) Ridge regression, and (v) Lasso regression. The suggested model should be thoroughly examined and discussed. Any possible software package, including SPSS, Minitab, Python, etc., is allowed. Association rules and the Apriori algorithm are also recommended for this section.

3. Finally, it is expected to discuss the findings. This encompasses a recap of findings, comparing the results with current literature, practical and managerial implications, limitations, and future recommendations.

The following structure is expected for the individual report.

The recommended word count for each section is as follows (2,000±10%).

An individual report template is also shared to follow the structure, style and format.  

Module Learning Outcomes

LO₁. Designed, changed, and innovated digital business models based on new technologies.

LO₃. Collect, analyse and interpret data analytics to make informed business decisions.

LO₄. Appraise how digital business and data analytics can generate actionable

insights for managers and decision-makers.

Grading Criteria