Learning Outcomes

1.   Discuss the concepts and methods of data analytics using relevant and appropriate terminologies.

2.   Design a data analytics project with a critical assessment of the data mining process and techniques involved in collecting, managing, and modelling actionable data.

3.   Use a range of descriptive analytics techniques to discover, visualise and interpret patterns in a large amount of data.

4.   Apply predictive analytics to predict future outcomes and model scenarios to address a range of business problems.

5.   Evaluate and communicate insights derived from data to a critical audience and make them effective in actual business decision-making.

Assessment Instructions

The purpose of this assessment is to demonstrate your ability to implement a data analytics project in business. You will work individually to analyse a data set using appropriate data mining approaches to make predictions about future outcomes. For this assessment, you are required to produce a 3,000 words business report, which should describe the methodologies and results of data exploration and analyses you have conducted. In addition, your report will also show that you have read recent, relevant academic and practitioner literature to explain the managerial relevance of your work.

For the task, you are to assume that you are a business analyst in an organisation (of your choice). The managers of the selected organisation would like to understand the sector they are  competing  in from  either  a marketing or management perspective  and  in  order to complete this task, you will need to analyse ONE of the two data samples provided by the tutor or any other publicly accessible data source3 .

This collected data will be used to analyse the selected organisation’s (and competitors’) business performance and from this analysis develop an understanding of the competitive position of the selected organisation. More specifically, you are required to analyse relevant business   indicators,   predict   the   relationships   between   the   business   indicators   and performance, and develop insights into your company’s current position against the selected

competitors  based on the data analysis and theoretical  understanding. A  list of  possible business questions will be provided with the instructor’s database.

Your report will be assessed on the basis of the following tasks:

1. Q1: Justifying and applying appropriate data analytics techniques to predict/forecast a business outcome. (30%).

2. Q2:  With  reference  to  recent  academic  articles  or  industrial  report,  analyse  the management  implications  of  implementing  the  business  decisions  you  suggested (30%).

3. Q3: Using appropriate descriptive analytics to overview the dataset. (20%).

4. Q4: Designing a data analytic with justification of the collected data (10%).

5. Q5: What are the limitations of your data analytics projects? (10%)

Note:

(i.)        Reading beyond the course materials is vital.

(ii.)       The use of graphs and diagrams to illustrate your analysis is strongly encouraged.

(iii.)      You are reminded that this assignment is about Business Analytics, not data science

nor statistics. The  mathematical justification and specific  number of analyses you applied is not as important as your ability to conduct a clear analysis of the business implications of those data analytics technique.

(iv.)      It is recommended that your work should have between 10 to 20 references. Please

use Harvard referencing throughout.

(v.)       Additional information e.g., background about the organisation you are studying can

be provided as an appendix of your report.

(vi.)      Consider how to best present your report to make it look as professional as possible

e.g. using a contents page. Tables and Figures must be labelled with a caption, “Figure 1: Diagram of… .” etc. with the text referring to the Figure, or table, as Figure 1, etc.

Suggested Report Structure

1. Title of the report

o The title should be informative and concise.

2. Introduction

o Provide brief background information of your chosen company and competitors.

o Highlight the objectives of the data analysis.

o A quick map/outline of the report (how the report is structured).

3. Main body – Methods, Results/Findings and Discussions

Data Collection

o Describe the data collection process (e.g., what data is collected, what variables are selected and from which database etc.).

o Illustrate steps for data cleaning and how the final sample is created (e.g., how did you  deal with the  missing values)  | Note: This  is  likely to  cover  part of your response to Q4 |

Data Description and Summary

o Report the results for Q1 and Q3.

Present descriptive statistics of the  key  indicators  required  in Q3 (e.g.,  mean, standard deviation, minimum/maximum values etc.) and relevant findings on the indicators.

Results/Findings and Discussion

o Present and interpret data analysis results for Q1.

o Discuss  findings  based  on  the  data  evidence  and  the  provided  theory  in  the readings. Critically evaluate your model and results with wider literature. Q1 and Q5.

4. Conclusions

o Summarize the major findings and your conclusions drawn from the data analysis.

o Identify  the  limitations  of  data  analysis  in  the  report  and  suggest  for  future research.

5. References

o List the works or resources you have referred to in the report or used to research (e.g., books, academic articles, industry report, websites, etc.)

o Harvard referencing style (Note: reference list and in-text citations)

6. Appendix

o You must include a Python notebook as an attachment.

o Additional supporting information or research that is too detailed or not essential to be included in the main body (e.g., tables, charts, raw data, formulas, a glossary of terms used etc.).

o The raw dataset and detailed calculation should not be presented in the appendix.

o If you are using your own dataset, please submit your raw materials via the data sample submission point. You don’t need to submit the raw dataset if you are using the instructor’s sample.