Global Salaries in Cybersecurity

2024-01-16 282 words 2 minutes

Contents

Conducts an analytical examination of global cybersecurity salaries to elucidate the various factors impacting compensation in this field. Click here to check it on GitHub

Global Salaries in Cybersecurity Analysis

Overview

This project involves the analysis of Global Salaries in Cybersecurity to gain insights into various factors influencing salaries. The dataset contains information on work years, experience levels, employment types, job titles, salaries, and other relevant features.

Steps Taken

Step 1: Data Exploration

Imported necessary libraries, including pandas, numpy, seaborn, and matplotlib.
Loaded the dataset into a pandas DataFrame (df_cleaned).
Conducted initial exploration using functions such as info(), describe(), and head() to understand the structure and contents of the dataset.

Step 2: Data Cleaning

Handling Missing Values:
- Checked for missing values (none found).
Data Type Check:
- Checked and adjusted data types for each column as needed.
Duplicate Check:
- Checked and removed duplicate rows (none found).
Identifying Outliers:
- Used the interquartile range (IQR) method to identify and remove outliers.
- Explored log transformation as an alternative approach to handling outliers.

Step 3: Exploratory Data Analysis (EDA)

Salary Distribution and Trends:
- Explored the overall distribution of salaries across work years and experience levels using box plots.
- Analyzed how average salary changed for different employment types over time using point plots.
Factors Influencing Salary:
- Explored average salaries by experience level, employment type, and company size using bar plots.
- Visualized the salary distribution for top 10 job titles.
Career Exploration and Insights:
- Explored the highest-paying job titles in the dataset.
- Analyzed which countries offer the highest average salaries for specific job roles.

Author

Feel free to contact me for any questions or additional information about this project.