The project followed a structured approached and used a project plan to set objective and milestones. The entire project was performed using an iMac and MacBook which ran Parallels desktop to facilitate a Windows environment to use the desktop version of Power BI. I needed to use data cleaning, analysis, and visualisation skills to undertake

The gender pay gap remains a persistent issue across industries, demanding careful analysis and interpretation of data to drive meaningful change. We can use Power BI to shed light on the disparities and advocate for gender equality in the workplace. As the data had already been cleaned and filtered, potential bias and limitations were accounted

Before delving into the analysis, it is essential to comprehend the gender pay gap data being used in the investigation. The data includes job roles, industries, and organisational sizes. Statistical analysis Statistical methods can provide valuable insights into the gender pay gap data. This can be used to examine the trends in the gender pay

General starter file There were several steps to preparing the general starter file for analysis. This included changing the format of several columns, such as the due date column, to a year format. The quartile data columns needed to be changed to 0 decimal place. It was also important to remove any unnecessary data completed

The quality and reliability of datasets play a crucial role in the success of any project. Datasets are not immune to bias and quality issues, and if not dealt with, these can have consequences for data analysis. Data Bias Bias in datasets refers to unfair or unrepresentative elements that can skew the results. It can

All datasets needed to be uploaded to OneDrive. This is something I am familiar with and I had no issue in utilising OneDrive as required. It is vital to store datasets securely when using these for analysis. As good practice, ensuring data is stored and only accessible by those who require it again follows the

General starter file The starter file uses the UK Government’s gender pay gap data set. The data has been cleaned to remove some columns such as the mean and median bonus percentages, male and female bonus percent and responsible person. Removing the responsible person’s data allows us to follow the Data Ethics Framework and GDPR

The planning stage was a straightforward process to undertake as part of the Data Science Project. As an allocated data set would be used, this provided a starting point that allowed me to begin investigating the area where I wished to focus my investigation. Defining the problem I split the planning stage into six sections