Data Analytics Portfolio 2000 Words

LinkedIn Learning Courses

You are required to complete the following LinkedIn Learning Courses in independent learning time:

1. Introduction to Formulas and Functions

2. Introduction to Charts and Graphs

3. Introduction to Formatting

4. PivotTables for Beginners

Individual Report with Reflection

You are a junior member of the team in an organisation within the department/industry aligned with your degree pathway e.g. marketing, finance, law, sports, economics etc.

You have been tasked with producing a report investigating the potential use of data analysis within the organisation detailing the benefits it can bring in terms of decision making at present and in the future.

In your report, you are required to discuss data analytics vs business intelligence and types of data analytics. You must also discuss potential problems associated with data bias in decision making. You should make recommendations as to how the organisation could make use of data analysis to aid in decision making.

Your organisation has provided some data for you to analyse and interpret to produce insights as part of your report to support your business case.

You are required to use Microsoft Excel for your data analysis.

In your report, you should include the following main sections:

1.Cover page.

2.Table of Contents.

3.Introduction

a.Background to your role/task

b.The importance of data

c.What is data analysis?

4.Data Analysis

a.Discussion of business intelligence versus data analytics

b.You should identify the five different methods of data analysis

c.You should analyse and interpret your data to generate insights that would help decision making within your organisation

d.You should identify and discuss common sources of data bias as a limitation (and link to your analysis)

5.Conclusion – A summary of the report and recommendation for future use of data analysis in your organisation.

In your reflection, you should consider your practical experience, knowledge and skills relevant to your degree, and contributions to your employability. You may reflect on your competencies in data analysis, the knowledge and expertise you gained through the module, your personal development, and the development of your confidence and responsibility. Do not be afraid to reflect on your weaknesses, limitations or challenges that you faced while working on the report. You should consider how you overcame them, the actions you took, and what were the lessons learned for you.

For reflection purposes, you should use a reflective model such as Kolbs Learning Cycle, Gibbs Reflective Cycle, or any other appropriate reflective tool. This is a good opportunity to work with a different reflective model so that you have experience of applying different approaches.

In your reflective statement, you should include the following main sections:

6.An Introduction to your reflective statement (including your chosen reflective model).

7.The reflection section – this will create the main body of your reflective statement.

8.Conclusion of your reflection and future actions to take, including updates to your personal development plan / Portfolio of Professional Evidence.

9.Reference List (using APA 7th Edition of the Harvard Referencing Scheme) for report and reflection.

N.B. In your reflection section you should consider the following:

• your own understanding in relation to data analysis for business prior to the module
• identification of skills you have developed as well as areas for further improvement
• your experience, any challenges, and the knowledge and expertise you gained throughout the module, report and personal development
• your weaknesses, limitations or challenges you faced and how you overcame them, the actions you took, and what was the learning lesson for you.

Students are permitted to use Generative Artificial Intelligence (GenAI) tools (e.g., ChatGPT, GrammarlyGO, Microsoft Copilot) to support their learning and assessment preparation for this module. However, any use of GenAI must fully comply with the University of Salford’s guidance on the ethical and transparent use of such tools.

You must ensure that:

• Any GenAI-generated content is clearly acknowledged and appropriately integrated into your original work.
• GenAI is not used to generate entire sections of your assessment or to bypass independent critical thinking and analysis (including any part of your data analysis).
• The work you submit remains your own and meets the academic integrity standards set by the University.