Skip to content
Glassdoor Data Analysis on Power BI
  • AI Chat
  • Code
  • Report

    • Introduction

    Glassdoor is an American website where current and former employees anonymously review companies.Glassdoor also allows users to anonymously submit and view salaries as well as search and apply for jobs on its platform.

    In 2018, the company was acquired by the Japanese firm Recruit Holdings for US$1.2 billion. It continues to operate independently.The company is headquartered in San Francisco, California, with additional offices in Chicago, Dublin, London, and São Paulo.

    Download the Power BI source template file below for further review.

    https://github.com/yangoo9/Glassdoor-Data-Analysis-with-PowerBI/blob/main/Glassdoor%20Data%20Analysis.pbix

    import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
    df_glassdoor = pd.read_csv('glassdoor.csv')
df_glassdoor.head(5)

    Glassdoor candidate dataset to analysis on income proportions, Distribution, and Anomalies. Further decomposition of employee attrition and their feedback, and finding top segmentations by critical influencers.

    (There are many attributes to analyse data but it's utilize by common relationship and correlartion between incomes, job role, level, job satisfaction, gender, age group, carrer stage ,education, no of candidate review, total working year, employee's year of last promotion, ,rolling avg for time-series.

    To Summarize, more than 30% of Research doctors, managers, and healthcare reps are aged around 25 - 34 years old and are happy at their jobs. They are higher education holders and others. $8.8K Avg income salary of mid-level professionals is likely to be doctor level education and managers job roles.

    Anomalies candidates reviewed were found on Jun 24 and 30th. Impressive numbers by the female candidate are unexpectedly higher and lower in 30 days of the rolling average period.

    Overall, 40% of attrition rates are in 2021. The highest group of employee attrition are mostly males who are single and aged between 25 - 34. Their job roles are Lab Technician and high-ranking job level. The lowest attrition rates of employees have primarily divorced females who worked as Research scientists holding high-ranking job roles and aged between 35 - 44.

    The outcomes of the plot show when the income increase, the job role called Manager is most likely to increase. Among top segments, Managers with more than 20 years of working experience are the highest earnings, which is approximately avg. $12.5 K more elevated than the overall avg. The top segment contains 25 data points (5% of the data). Looking at the total working experience and more extended years of serving in a company can increase employee income salary. Its strong positive correlations between those attributes.

    Job role called Sales Executive is in the lower segment. Avg $ 3.7K is more inadequate than the overall average. It has 77 data points (15% of the data).

    1. Income Proportions

    1. Job roles called Manager is the most highest average monthly income but candidarte reviewed is not as high as Lab Technician, Research science and sales executive roles.

    2. Overall 95% of significant correlation of candidate reviewed by employee's income and their job level.

    3. 39.9% of most job satisfaction can be found in age between 25 - 34 and those are specifically Reseach Directors, Managers and Healthcare workers. Their proportions are 48.9%, 36.67% and 38.04 and highest in a row.

    2. Distribution and Anomalies

    1. Box and Whisker plot describe mean of income review based on career stage and education. The outcomes of LHS box plot shown Mid career has the avg salary income of 2 K and maximum of $19 K in 2021. Those who revceived amount of incomes are mostly Doctor level of education according to the (RHS) plot.

    2. Line chart describe the 30 days of rolling avg to find the anomalies, that occurs in the end of the june 24 and june 30. In june 24, number of candidate review has unexpectedly increase over the expectation however in 30th june it went down.

    3. RHS Line chart has breakdown into genders, and it obviously see Female candidates are unusally lower than the trends and most anormalies goes to this segment.

    3. Decomposition of employee attrition and their feedback

    1. Overall 40% of attrition rates are on 2021. The highest group of employee attrition are mostly male who are single and age between 25 - 34. Their job roles are Lab Technician and high ranking job level.

    2. Lab Technicians are some what unhappy and complaint about office politics, poor work environments, complaint about low paying and lack of proper work life balance.

    3. Lowest attrition rates of employee are mostly divorced female who worked as Research Scientist holding high ranking job roles and age beteween 35 - 44.

    4. Finding Top segmentation by key influencers

    1. The outcomes of the plot shown, when the income increase the job role called Manager is most likely to increase.

    2. Among top segments, Manager with more than 20 years working experienced are the highest earned. Which is approximately avg. $12.5 K higher than overall avg. The top segment contains 25 data point and (5% of the data). The fact that look at total working experience and longer year of serving in a company can increased employee income salary. Its stron positive corelatio between those attributes.

    3. Job role called Sales Executive are in lower segment. Avg $ 3.7K lower than overall average. It has 77 data points (15% of the data)

    ‌
    ‌
    ‌