Welcome to my Python Pandas data analysis project! In this project, I delve into the dynamic field of data science using Python libraries to manipulate, analyze, and visualize datasets. My focus is on exploring various aspects of the dataset, from dropping unnecessary columns to uncovering insightful trends and patterns.
Project Highlights:
Data Manipulation: I start by cleaning and organizing the dataset, dropping unnecessary columns to streamline my analysis. Salary Analysis: Utilizing Python Pandas, I calculate the average salary by year and explore the distribution of salaries across different company sizes. Employee Distribution: I examine the ratio of remote and non-remote employees to gain insights into modern work trends. Visualization: Throughout the analysis, I create informative charts and visualizations to present my findings effectively. Exploratory Analysis: I investigate the company size based on the years and identify the top job titles within the dataset. Histogram: A histogram showcasing the distribution of salaries by company size provides a comprehensive view of salary trends. Experience Level: Finally, I determine the experience level of employees to understand the workforce dynamics.
Screenshots: You can find screenshots of my analysis results in the "Screenshots" folder. These images provide visual representations of the insights and trends uncovered during my data analysis journey.
Files Included:
Data_Analysis_01.ipynb: The Jupyter Notebook containing the Python code for my data analysis project. data_science.csv: The source CSV file containing the dataset used in my analysis.
Instructions:
- Clone or download the repository to your local machine.
- Open the data_analysis_project.ipynb file in Jupyter Notebook or any compatible platform.
- Run the cells sequentially to execute the Python code and reproduce the analysis.
- Refer to the screenshots folder for visual representations of the analysis results.
I hope you find this data analysis project insightful and informative! Feel free to reach out with any questions or feedback. Happy exploring!