Introduction
In this project, data cleaning, exploration and visualization was done using only Microsoft excel.
Analysis was done to to extract meaningful insights about the distribution and characteristics of the fellows across different host companies
Data structure:
This dataset originates from the HR department of the company. The dataset contains records of employees ranging from Company Name, Company Location, Fellow Name, Fellow Educational Background, Fellow Age, Fellow Gender, Fellow ID, Fellow Start Day etc.
Data cleaning and preparation:
The data cleaning and preparation phase was done to ensure the dataset is free from errors, outliers and duplicates.
Here is a picture of the dataset prior to cleaning:
Some of the steps taken to clean the data include:
1.Removal of duplicates
2.And the removal of Blank rows and columns
Here is a picture showing how the blank rows were removed from the dataset.
Analysis was done using pivot table and charts.
Slicers were also added for easy access to various niche of the dashboard.
The dashboard provides answers to the following :
1.Count of Fellows by Age
2.Count of Fellows by Gender
3.Top Ten companies with the most Fellows
4.Count of Fellows by Company Location
Here is a picture of the dashboard:
1)Count Of Fellows By Age
In this dataset, the age distribution of fellows reveals significant insights:
•Fellows Over the Age of 33: 3,861
•Fellows at the Age of 22: 2,024
This data shows that there is a higher number of fellows over the age of 33 while fellows over the age of 22 are the least among the fellow age groups.
The substantial number of fellows over the age of 33 suggests a more experienced workforce. This can be advantageous for roles requiring significant expertise and leadership. However, it also highlights the potential need for succession planning.
2)Count Of Fellows By Gender
In this dataset, the count of fellows by gender is as follows:
•Males:739
•Females:767
This indicates a slight majority of female fellows, who constitute approximately 50.93% of the total, while males make up about 49.07%
The near-equal gender distribution is indicative of a balanced gender representation, which is a positive sign for diversity and inclusion efforts.
3)Top Ten Companies With The Most Fellows
In this dataset, I analyzed the top ten companies with the most fellows. Notably:
•Health LTD has the highest number of fellows at 68.
•Build Holdings has the least among the top ten, with 38 fellows.
Companies with a higher number of fellows, like Health LTD, might have more robust talent acquisition and retention strategies. Conversely, Build Holdings, with fewer fellows, might benefit from insights into the strategies employed by higher-ranking companies to enhance their own talent pool.
In this dataset, the count of fellows by company location reveals the following:
•Gombe: Has the highest number of fellows at 319.
•Bauchi: Has the least number of fellows among the locations analyzed, with 133.
The significant number of fellows in Gombe suggests a strong talent pool in this location. This could be due to various factors such as the presence of major educational institutions, a favourable job market, or specific industry clusters.
