Exploratory Data Analysis (EDA) of global Data Analyst / Data Science salaries. This is a practical, portfolio-ready project that walks through loading, cleaning, analyzing, and visualizing a salary dataset to answer questions that matter to anyone entering or growing in the data field.
This project answers real questions using data:
- How does salary scale with experience level (Entry β Mid β Senior)?
- Which job titles command the highest pay?
- Which countries / company locations pay the most?
- Does remote work correlate with higher salaries?
data-analyst-salary-analysis/
βββ data/
β βββ data_analyst_salaries.csv # Dataset (40 records, 9 columns)
βββ analysis/
β βββ salary_analysis.py # EDA: load, clean, analyze, visualize
βββ reports/ # Generated charts (created on run)
βββ requirements.txt # Python dependencies
βββ LICENSE # MIT License
βββ README.md
The dataset contains anonymized salary records with the following columns:
| Column | Description |
|---|---|
work_year |
Year the salary was recorded |
experience_level |
EN (Entry), MI (Mid), SE (Senior), EX (Executive) |
employment_type |
FT (Full-time), PT (Part-time) |
job_title |
Role title (Data Analyst, Data Scientist, etc.) |
salary_usd |
Annual salary in USD |
employee_residence |
Country code of the employee |
remote_ratio |
0 = on-site, 50 = hybrid, 100 = fully remote |
company_location |
Country code of the company |
company_size |
S (Small), M (Medium), L (Large) |
- Clone the repository:
git clone https://github.com/SnakeEye-sudo/data-analyst-salary-analysis.git cd data-analyst-salary-analysis - Install dependencies:
pip install -r requirements.txt
- Run the analysis:
python analysis/salary_analysis.py
The script prints summary statistics and saves a chart to reports/salary_by_experience.png.
- Experience pays off: Senior roles earn substantially more than entry-level positions across every market.
- Title matters: ML Engineer and Data Scientist roles top the pay scale, ahead of generalist Data Analyst roles.
- Location drives pay: US-based roles lead, followed by Canada and the UK; the same title can pay very differently by country.
- Remote correlation: Fully remote roles tend to align with higher average salaries in this dataset.
- Python 3 β core language
- pandas β data loading, cleaning, aggregation
- matplotlib β visualization
This project is licensed under the MIT License β see the LICENSE file for details.
Built by Sangam Krishna (@SnakeEye-sudo) β Web App Developer & Data Analyst.
Exploratory Data Analysis (EDA) of global Data Analyst / Data Science salaries. This is a practical, portfolio-ready project that walks through loading, cleaning, analyzing, and visualizing a salary dataset to answer questions that matter to anyone entering or growing in the data field.
This project answers real questions using data:
- How does salary scale with experience level (Entry β Mid β Senior)?
- Which job titles command the highest pay?
- Which countries / company locations pay the most?
- Does remote work correlate with higher salaries?
data-analyst-salary-analysis/
βββ data/
β βββ data_analyst_salaries.csv # Dataset (40 records, 9 columns)
βββ analysis/
β βββ salary_analysis.py # EDA: load, clean, analyze, visualize
βββ reports/ # Generated charts (created on run)
βββ requirements.txt # Python dependencies
βββ LICENSE # MIT License
βββ README.md
The dataset contains anonymized salary records with the following columns:
| Column | Description |
|---|---|
work_year |
Year the salary was recorded |
experience_level |
EN (Entry), MI (Mid), SE (Senior), EX (Executive) |
employment_type |
FT (Full-time), PT (Part-time) |
job_title |
Role title (Data Analyst, Data Scientist, etc.) |
salary_usd |
Annual salary in USD |
employee_residence |
Country code of the employee |
remote_ratio |
0 = on-site, 50 = hybrid, 100 = fully remote |
company_location |
Country code of the company |
company_size |
S (Small), M (Medium), L (Large) |
- Clone the repository:
git clone https://github.com/SnakeEye-sudo/data-analyst-salary-analysis.git cd data-analyst-salary-analysis - Install dependencies:
pip install -r requirements.txt
- Run the analysis:
python analysis/salary_analysis.py
The script prints summary statistics and saves a chart to reports/salary_by_experience.png.
- Experience pays off: Senior roles earn substantially more than entry-level positions across every market.
- Title matters: ML Engineer and Data Scientist roles top the pay scale, ahead of generalist Data Analyst roles.
- Location drives pay: US-based roles lead, followed by Canada and the UK; the same title can pay very differently by country.
- Remote correlation: Fully remote roles tend to align with higher average salaries in this dataset.
- Python 3 β core language
- pandas β data loading, cleaning, aggregation
- matplotlib β visualization
This project is licensed under the MIT License β see the LICENSE file for details.
Built by Sangam Krishna (@SnakeEye-sudo) β Web App Developer & Data Analyst.
π Exploratory Data Analysis of global Data Analyst / Data Science salaries β pandas, data cleaning, insights & visualizations. A practical, portfolio-ready DA project.