This is a Power BI dashboard focused on AI, ML, and Data Science Salary Analysis (2020-2025). Here’s a breakdown of the key elements in the dashboard:
Dataset Link is given here : https://www.kaggle.com/datasets/samithsachidanandan/the-global-ai-ml-data-science-salary-for-2025
1. KPI Metrics (Top Section)
- 88.58K → Count of Records (Total number of salary records analyzed)
- $157.57K → Average Salary (Mean salary for AI, ML, and Data Science roles)
- $15K → Minimum Salary (Lowest salary recorded)
- $800K → Maximum Salary (Highest salary recorded)
- $73.53K → Standard Deviation in Salary (Variation in salary distribution)
2. Visualizations (Middle & Bottom Section)
Countrywise Avg Salary (Map)
- A geographic heatmap showing average salaries by country.
- Darker shades likely indicate higher average salaries.
Company-size wise Avg Salary (Bar Chart)
- Compares salaries across Small (S), Medium (M), and Large (L) companies.
- Larger companies tend to offer higher salaries.
Avg Salary by Employment Type (Donut Chart)
- Shows salaries for different employment types: Full-time (FT), Contract (CT), Part-time (PT), and Freelance (FL).
- FT employees seem to have the highest proportion of salaries.
Avg Salary by Work Year and Company Size (Line Chart)
- Trends salary growth across years (2020-2025).
- Compares salaries in small, medium, and large companies.
Top 5 Job Titles by Avg Salary (Bar Chart)
- Displays highest-paying job roles in AI, ML, and Data Science.
- Includes titles like Data Scientist, Machine Learning Engineer, Applied AI Engineer, etc..
Avg Salary by Company Size and Experience Level (Stacked Chart)
- Compares salaries based on company size and experience level:
- EN (Entry-Level)
- EX (Expert)
- MI (Mid-Level)
- SE (Senior-Level)
3. Filters (Right Sidebar)
Users can filter the data using:
- Company Location
- Company Size
- Employment Type
- Experience Level
- Job Title
- Remote Ratio
- Work Year
Conclusion
This interactive Power BI dashboard allows users to analyze salary trends in AI, ML, and Data Science based on different factors like company size, experience level, job title, and geography. It helps professionals and organizations make data-driven salary decisions.