BS657 L7 Data Analysis for Business (DAB) Assessment Brief 2026 | ULaw

ASSESSMENT BRIEF

L7 Data Analysis for Business

By sitting this assessment, I am confirming:

That I have worked independently on this assessment submission, and I have not worked together with any current or previous student at the University to produce my submission, other than when officially permitted to do so;

• I also confirm the contents of my submission have not been generated by a third party;
• I have fully referenced and correctly cited the work of others, where required;
• I have not used any generative AI tools to generate, rephrase or otherwise produce content for this submission, except where their use was explicitly permitted, and I have read and understood the University's AI in Higher Education Policy and Protocols;
• I have read the Student Discipline Regulations and understand that any academic misconduct can lead to disciplinary consequences and undermine academic integrity;
• I understand that where applicable, I am expected to engage with my academic work in a manner that meets the professional standards and requirements set by the relevant Professional, Statutory and Regulatory Body (PSRB) or accrediting body for my course.
  By submitting this assessment submission, I am confirming that I am fit to sit according to the Assessment Regulations.

Learning outcomes: 

1.Critically evaluate and understand how data contribute to business success
2.Awareness of and understanding of the application of the Python programming language.
3.Understand and the ability to apply basic statistical techniques like linear regression and logistic regression.
4.Understand and ability to apply A/B testing and hypothesis testing.
5.Understand the basic machine learning concepts and critically assess how they can be applied in the world of business.

Assessment details: Individual written report, assessment weighting 100%. The report’s word limit is 2,500.

Referencing: Students are expected to use Harvard Referencing throughout their assignments where required. Please follow the Harvard Referencing Handbook for all your assignments at the ULBS. 

Submission Method: Turnitin - Your work will be put through Turnitin. All submissions will be electronically checked for plagiarism and the use of AI software. 

You have the option to upload your work ahead of the deadline, more than once. ULBS will be reviewing your last submission only. You can only upload one file. For example if your work contains a word document and power point slides/Excel spreadsheet you will need to copy your slides/spreadsheet into the word document.
Note: Keep in mind that self-plagiarism (when you reuse your own specific wording and ideas from work that you have previously submitted without referencing yourself) is also a form of plagiarism and is not allowed.
ULBS Assessment Office Contact Details
The ULBS Assessment Office are here to help should you have any non-academic questions related to your assessments. You can contact them at AssessmentOffice@law.ac.uk 

ASSIGNMENT DETAILS

This assignment aims to consolidate your understanding of the concepts, models and techniques discussed during the module and demonstrate your ability to apply data analytic thinking to support the decision-making process for real business problems. 

Assignment description

For this assessment, you are required to identify a real-world business problem that can be addressed using data-driven analysis. The goal is to engage critically with a business issue, source and analyse relevant data, and generate meaningful insights to support decision-making. Several university approved online sources for data are listed in the appendix 1. 

Your final submission should be presented in a structured report format (suggested report format and word count in Appendix 2). This report must demonstrate your ability to define and analyse the new business problem, process the data, apply appropriate analytical techniques, and interpret your findings. The report should include well-supported analysis and reference both academic literature and real-world examples.

Selecting the problem, you will investigate and discuss the following questions in your report:

1.Business Problem Identification (LO1 and LO5): 

Choose a real-world business problem that you believe can benefit from data-driven analysis. Ensure that the problem is well-defined and has relevance to the context of business.

In your report, explain why this problem is significant, the benefits of solving it, and who would benefit from the solution. Your critical analysis should draw on both academic literature and reputable online sources to support your reasoning.

2.Data Acquisition and Preprocessing (LO2):

Analyse and discuss the data required to address your chosen problem and explain how and where you will obtain it. Evaluate the key characteristics of the dataset, including its structure, features, and any potential limitations against the business problem. Outline the data preprocessing steps necessary to prepare the data for analysis, such as cleaning, transforming, and handling missing values, based on concepts learned in this module.

3.Data Analysis and Interpretation (LO3 and LO5): 

Propose at least two data analytic methods that can address your chosen problem. Apply critically one scenario where a regression analysis is suitable, and another where classification would be applied. Clearly explain the objectives of each analysis and model(s) used and how they relate to the problem. Note that these tasks may require using more than one dataset.

4.Hypothesis Testing and A/B Testing (LO4):

Critically evaluate different approaches for validating your analysis, including hypothesis testing, A/B testing, or other relevant methods like cross-validation or simulation studies. Choose the most appropriate validation method for your business problem, providing a thorough justification based on the data characteristics and problem context. Describe the implementation steps and potential outcomes, ensuring to illustrate your reasoning with examples or case studies.

5.Findings: 

Present the key findings from your analysis in a clear and concise manner. Provide actionable recommendations that are easy to understand for a non-technical audience. Your conclusions should be based on your data analysis and supported by evidence from your research.

6. Include the URL of your Google Collaborate in your report.

You must discuss the above questions by providing the appropriate references using academic and online sources. 

Appendix 1: Recommended sources of data

Reminder: Students are expected to follow university ethics when collecting data. If you want to collect data from a source other than those listed below, check with your tutor first. 

There are several online open sources for data analysis practice. 

Some of them are listed below: 

• Statista - Access through the library
• Dataset Search (google.com)
• kaggle datasets 
• Datasets | fp20 analytics
• Data.gov.uk 
• Data.world 
• Databank.worldbank.org 
• Research datasets | Bank of England
• Yahoo Finance 
• Cryptodata 
• UNICEF DATA
• Open Data on AWS 
• Nasdaq Data Link
• Home | Open Government Data (OGD) Platform India
• Data.gov Home - Data.gov
• Stack Exchange Data Explorer
• Curated open data · GitHub
• Academic Torrents

Reminder: If you want to collect data from another source, other than those listed above, check with your tutor first. Data should be sourced, stored and handled ethically. If in doubt, check with your module tutor.

Appendix 2: Suggested Report Format

The report should contain: 

• Title page 
• Executive summary (included in the word count approximately 100 words) 
• Contents page 
• Introduction (included in the word count approximately 250 words) 
• Outline the proposed business problem (150 words) 
• Main section which should include subsections for tasks 1-4 (included in the word count approximately 1,250 words) 
• Findings (included in the word count approximately 500 words) 
• Conclusion (included in the word count approximately 250 words) 
• Reference list 
• Appendices 

Section headings, table or chart headings or footers, table or data, page numbers, references and direct quotes do not count towards the word count. 

Please refer to the marking criteria below for a breakdown of how the tasks will be marked

BS657 L7 Assessment Criteria