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https://github.com/bineet-ratna-shakya/data-science-salary-analysis

analyzing a dataset containing salaries of data science professionals from 2020 to 2023.
https://github.com/bineet-ratna-shakya/data-science-salary-analysis

data-analysis data-science data-visualization jupyter numpy pandas python

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analyzing a dataset containing salaries of data science professionals from 2020 to 2023.

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README 

        
# Advanced Data Science Analysis



## Table of Contents

1. [Introduction](#introduction)

2. [Objectives](#objectives)

3. [Data Understanding](#data-understanding)

4. [Data Preparation](#data-preparation)

5. [Data Analysis](#data-analysis)

6. [Data Exploration](#data-exploration)

7. [Conclusion](#conclusion)

8. [References](#references)



## Introduction



This project is aimed at analyzing a dataset containing salaries of data science professionals from 2020 to 2023. The analysis focuses on various aspects such as salary trends, job titles, experience levels, and more.



## Objectives



The objectives of this data analysis project include:



- Analyzing salary trends over the years.

- Evaluating the impact of experience levels on salaries.

- Examining job titles and their corresponding salaries.

- Detecting and handling outliers in the salary data.

- Visualizing data distribution using various graphical representations.



## Data Understanding



### Dataset Description



The dataset contains information about salaries of data science professionals. It includes the following columns:



- work_year: The year the data was recorded.

- salary: The salary amount in the local currency.

- salary_in_usd: The salary amount converted to USD.

- remote_ratio: The percentage of remote work.

- experience_level: The level of experience of the employee.

- employment_type: The type of employment.

- job_title: The job title of the employee.

- salary_currency: The currency in which the salary is paid.

- employee_residence: The country of residence of the employee.

- company_location: The location of the company.

- company_size: The size of the company.



### Data Overview



- Entries: 3,755

- Columns: 11



## Data Preparation



### Loading Data



The first step in the analysis process is to load the data using Python's Pandas library.



### Removing Unnecessary Columns



Removing columns that are not needed for the analysis.



### Handling Missing Values



Identifying and handling missing values in the dataset.



### Checking for Duplicate Values



Checking for and handling duplicate values in the dataset.



### Viewing Unique Values in Columns



Counting the unique values in each column.



## Data Analysis



### Summary Statistics



Calculating various summary statistics to understand the dataset better.



- Sum: Total of all salary values.

- Mean: Average salary.

- Standard Deviation: Measure of the amount of variation in salary.

- Skewness: Measure of the asymmetry of the salary distribution.

- Kurtosis: Measure of the "tailedness" of the salary distribution.



### Correlation Analysis



Calculating the correlation between different variables to understand their relationships.



## Data Exploration



### Top 15 Job Titles



Analyzing the top 15 job titles in terms of frequency.



### Job Titles with the Highest Salaries



Analyzing the job titles with the highest salaries.



### Salaries Based on Experience Levels



Analyzing salaries based on different experience levels.



### Histogram and Box Plot for Selected Variables



Creating visual representations for salary distribution and work year.



## Conclusion



### Summary of Findings



The analysis revealed significant trends and patterns in the salaries of data science professionals. Key findings include:



- Trends in salary growth over the years.

- The impact of experience levels on salaries.

- Job titles with the highest demand and salaries.



### Implications of the Analysis



The insights gained from this analysis can help data science professionals understand salary expectations, assist employers in making informed salary decisions, and guide educational institutions in tailoring their programs to meet industry demands.



## References



- Brownlee, J. (2020). Data Preparation for Machine Learning. Machine Learning Mastery.

- Koehler, A. (2023). Practical Data Preparation. Data Science Journal.

- Pandas Documentation. (2024). pandas.pydata.org.