QHO430 Data Analysis, Tools and Application Individual Assessment 1 Report

QHO430 Assessment Brief 

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Assessment Task

The assessment includes both group work and individual research, focusing on data analysis. Groups of four to five students will discuss and choose a data analysis area to study. The available data is crucial for this choice and can cover various impactful topics such as the environment, population distribution, wealth, business, health, education, or international issues. The datasets can be obtained from various sources, including government organisations such as the Office of National Statistics (ONS) and GOV.UK. Additionally, the World Data Bank offers a vast collection of open datasets that could be considered. After selecting the analysis domain, each group member will define their individual topic of investigation within that domain. This will enable a detailed exploration of their chosen topic, allowing individuals to generate useful information that can be combined for the group presentation of findings.

In this assessment, you will focus on individual data analysis. You will analyse data based on a topic you choose within the group’s domain and produce an academic report. You should clearly define your individual data analysis objectives and follow the data analysis process outlined below:

• Collect raw data (unanalysed data). You can obtain it from a single source if it is suitable for multiple analysis techniques, or you can gather data from various sources to support your analysis objectives.
• Prepare your collected data by organising or formatting it appropriately for analysis.
• Utilise analysis tool such as Excel to analyse your collected data, employing a range of analysis methods suitable to your objective. These methods may include descriptive analysis, predictive analysis, or prescriptive analysis.
• Present your data analysis results using suitable data visualisation techniques that effectively convey the insights you wish to present.

Additionally, it is important to maintain thorough records of group meetings, discussions, and collaborations throughout the data analysis project. Your individual report should also include a self- reflection on your learning process during the module and the data analysis project.

At the end of the module, your individual data analysis and results will be summarised and combined into an insightful group presentation for your stakeholders.

Formatting of the Report

Cover Page

Table of Contents

Table of Figures

Report Contents

1. Introduction

a. A brief statement of your group project motivation/problems
b. Group project aim(s)
c. Your objective(s) for your analysis part, you can have more than one objective

2. Method

a. Data collection: discuss where did you get the data from (you can identify both the dataset use in a group and any specific dataset you use for your individual analysis); how did you retrieve the dataset. Ensure you reference sources for any data you collected or used.
b. Data preparation: identify process of data preparation that you perform on your collected data. The process can consist with all the following process or some of them depends on the suitability of the process and collected data.
i. data understanding, e.g., what variables that have been collected, how many variables in that dataset
ii. data cleaning e.g., handling missing value (if it’s needed)
iii. data pre-processing e.g., selecting certain variables to use for analysis, merging data from different file, sorting data etc.

c. Data analysis: identify data analysis techniques/approaches/methods that you use to get the results. Analysis methods need to match with your analysis objectives. You need to perform data analysis using analysis tool such as Excel based on your identified approaches

d. Results: present the analysis results of your analysis with appropriate data visualisation

e. Conclusion: conclusion or summary of your individual analysis results

3. Project Management

a. Project plan: present your work plan (tasks) and timeline using Gantt chart
b. Project collaboration: identify your skill of collaboration – how you are meeting up, how did you work as a group, evidence of group work collaboration e.g., snapshot of discussion board, online-diary, share document on MS team etc.
c. Learning reflection: discuss learning outcome you achieve; issues you have had and what you might do differently in the future

4. References

a. A list of citations for all sources you have referred to in the body of your report. These should all be in Solent Harvard reference format

Note: If you have any special requirements or disability, please discuss this with your tutor

REFERENCING

Note:
QHO430 Formatting Guidelines of the Individual Report Document

Specifications of the Individual Report Document

Data Analysis_Report_Template_2023 (2).docx

Use of AI in this Assessment

Generative AI is permitted at Solent University under specific conditions and must continue to follow the university’s rules around Academic Misconduct and the AI and Academic Integrity policy.

In this assessment, you are permitted to use GenAI tools, such as Large Language Models (LLMs) or grammar support tools, to assist in editing the content of your data analysis report or to suggest topics for your data analysis project. However, it is crucial that you carefully review any generated content to ensure that it maintains the original message you intend to convey. You may also make edits to improve the clarity, accuracy, or relevance of the generated text. Furthermore, it is your responsibility to check the correctness of the information produced by these tools.

If you choose to use a GenAI tool for these purposes, you are required to disclose your usage clearly. This means specifying which tool was used, identifying which parts of the content were generated by the tool, and highlighting any changes or edits you made after reviewing the output. Transparency in this process is essential.

However, it is important to note that you are not allowed to use GenAI tools to carry out any part of the data analysis process on your behalf. The analysis itself, including data identification, data understanding, preparation, analysis, and visualisation, must be performed entirely by you. This is necessary to fulfil the learning outcomes of the module, which emphasise the importance of understanding and executing the full analysis process.

Using GenAI tools to conduct the data analysis or generate results and claiming them as your own work constitutes a breach of academic integrity. This practice can lead to an unfair advantage and does not demonstrate your own understanding of the material. Therefore, you must perform the data analysis independently to ensure that your skills and knowledge are accurately represented.

 AI and Academic Integrity Policy

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QHO430 Assessment Criteria

 

Learning Outcomes

This assessment will enable you to demonstrate in full or in part your fulfilment of the following learning outcomes identified in the Module Descriptor:

1. Identify appropriate tools and techniques for data analysis, data visualisation and presentation.
2. Carry out small-scale research, information gathering and data collection to generate knowledge to support the project with some guidance.
3. Discuss the use of relevant data analysis tools.
4. Collaborate in groups on projects and work on each step of the data life cycle.
5. Summarise and present the results of data analysis to a range of stakeholders making recommendations.
6. Communicate and summarise and present the results of data analysis to a range of stakeholders making recommendations.

Living CV

As part of the University’s Work Ready, Future Ready strategy, you will be expected to build a professional, Living CV as you successfully engage and pass each module of your degree.

The Living CV outputs evidenced on completion of this assessment are:

1. Ability to use analytical tools such as MS Excel to analyse data using descriptive and predictive techniques and produce reports, charts and tables to present the results and data.
2. Ability to undertake small-scale research to acquire data from both primary and secondary data sources and analyse collected data.
3. Able to select analysis model that suitable with data sets
4. To work as part of a team and carry out my own initiative work.
5. To present the findings of analysis results in a clear and simple format for non-technical audiences

Please add these to your CV via the Living CV builder platform on Solent Futures Online Solent Futures

Important Information

Solent University Academic Regulations 2024-25

Late Submissions

You are reminded that:

1. If this assessment is submitted late i.e. within 7 calendar days of the submission deadline, the mark will be capped at 40% if a pass mark is achieved;
2. If this assessment is submitted later than 7 calendar days after the submission deadline, the work will be regarded as a non-submission and will be awarded a zero;
3. If this assessment is being submitted as a referred piece of work, then it must be submitted by the deadline date; any Refer assessment submitted late will be regarded as a non-submission and will be awarded a zero.

Assessment regulations

Extenuating Circumstances

The University’s Extenuating Circumstances (EC) procedure is in place if there are genuine short term exceptional circumstances that may prevent you submitting an assessment. You are able to self-certify for up to two assessment dates in any semester without supporting evidence for an extension of up to seven calendar days for coursework or to defer an exam to the resit period.

Alternatively, if you are not ‘fit to study’ (or you have used up your two self-certification opportunities), you can request:

• an extension to the submission deadline of 7 calendar days, or
• a request to submit the assessment at the next opportunity, i.e. the resit period (as a Defer without capping of the grade).

In both instances you must submit an EC application with relevant evidence. If accepted under the university regulations, there will be no academic penalty for late submission or non-submission dependent on what is requested. You are reminded that EC covers only short-term issues (20 working days) and that if you experience longer term matters that impact on your learning then you must contact the Student Hub for advice.

Please find a link to the EC policy below:

Extenuating Circumstances

Academic Misconduct

Any submission must be your own work and, where facts or ideas have been used from other sources, these sources must be appropriately referenced. The University’s Academic Regulations includes the definitions of all practices that will be deemed to constitute academic misconduct. You should check this link before submitting your work.

Procedures relating to student academic misconduct are given below: Academic Misconduct

Ethics Policy

The work being carried out must be in compliance with the university Ethics Policy. Where there is an ethical issue, as specified within the Ethics Policy, then you will need an ethics release or ethics approval prior to the start of the project.

The Ethics Policy is contained within Section 2S of the Academic Handbook:

Ethics Policy

Grade marking

The University uses a numeric grade scale for the marking of assessments. More detailed information on grade marking and the grade scale can be found on the portal and in the Student Handbook.

Grade Marking Scale

Guidance for online submission through Solent Online Learning (SOL)

Online Submission

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