代写BUSM211 Business Digital Analytics 2024/25代写C/C++语言

2025-04-03 代写BUSM211 Business Digital Analytics 2024/25代写C/C++语言

BUSM211

Business Digital Analytics

2024/25

Module Level Learning Outcomes to be assessed

 

No

Module Learning Outcome

 

Description

1

A2

Critically evaluate the performance of business strategies and tactics based on appropriate metrics

2

A3

Effective presentation of metrics and performance data (e.g. using analytics dashboards).

3

B2

Critical evaluation skills to identify key data and evaluation metrics that are aligned with business goals

4

B3

Effective communication skills, especially in data presentation

5

C2

An eye for detail and specific information (often numerical).

6

C3

Willing to deal with complex and vast quantities of information and distil insights from it.

Assurance of Learning (selected modules only): contribution to Programme Level Learning Outcomes

No

Programme Learning Outcome

Description

1

A4

Discerning the realm of marketing as a dynamic field through the changes in the digital global environment

2

A6

Critical engagement with quantitative and qualitative digital methodologies

3

B4

Develop Advance digital analytics via qualitative and quantitative research skills

4

B6

The ability to analyse and evaluate a range of business data, sources of information and appropriate methodologies

5

C1

Be able to independently on a piece of chosen area of research

6

C2

Develop effective communication skills and marketing strategies with case scenarios

Assessment instructions for students (as per QMPlus ‘Assessment Information’ tab)

1. The module learning outcomes being assessed See above.

2. Instructions and guidance

Assessment: Individual Report (100%), 3000 words all inclusive

Submission date: (TBC, Please refer to the QMplus module page)

The length of the report should be no more than 3000 words excluding references and appendices. Marks may be deducted if you overshoot the word limit. The stated word counts may be exceeded by a maximum of 5%. Please pay attention to writing and referencing style. The preparation of this individual report and the exchange of experiences in the classrooms or seminar meetings are major learning aspects of the Business Digital Analytics module.

Today many firms are sitting on large chunks of data, not knowing what to do with it. They invite consultants & business analysts to have a look at data and come up with insights that could help the organization make better business and marketing decisions. Such problems are mainly open-ended problems, and it is expected of the analyst/consultant to do a lot of exploration first before analysing the problems and recommending solutions.

This assessment requires you to explore an open data set (data-sets will be uploaded to QMplus) and come up with insights that will aid business decision making. You are required to prepare a report based on your findings which may be considered as briefing to the CMO who will use these insights for business decisions for the firm. There are no right and wrong answer here, however there should be a clear logical story which should come out of the analysis that support business decisions.

The report should demonstrate your understanding and application of the data analytics, insights for decision making and the tools discussed in this module. The report should explore descriptive, diagnostic, and predictive analytics. The report should include the following elements

· Introduction

· Business Problem

· Data Summary

· Methodology Adopted

· Results

· Discussions and Recommendations

The report should have a professional layout and report style appropriate for business/executive readers, writing is clear and to the point, logical/coherent arguments to support points and recommendations, charts/figures/dashboards, and tables (where applicable) to help readers understand key points etc

3. Assessment rubric with weighted criteria

Business problem identification and discussion on its importance (20%)

Data summary (10%)

Methodology discussion (10%)

Results (20%)

Interpretation and implications (20%)

Discussions and Recommendations: (20%)