Data Professional Survey Breakdown

This Power BI project presents an in-depth analysis of a global survey conducted among data professionals across various industries. The goal was to uncover meaningful patterns in their work experiences, compensation, satisfaction levels, and preferred technologies. This end-to-end analysis goes beyond static charts—offering an interactive and engaging experience through an elegantly designed dashboard. Users can filter and explore data dynamically based on roles, experience, and geography.

From Data Cleaning and Data Transformation to creating visual storytelling with Bar Charts, Gauge Charts, Pie Charts, and more — this project showcases both technical and analytical skills using real-world data. Tools like Model View were also used in other projects, to manage relationships between tables and maintain data model integrity.

Interactive Dashboard

Data Cleaning & Preparation Process

The journey began by importing raw survey data from an Excel file into Power BI. The dataset contained various inconsistencies and unstructured information that required thorough cleaning. Using Power BI's Power Query Editor, I performed a range of data transformation tasks to ensure accuracy and readiness for analysis. While working on creating visualizations, I encountered minor issues which were resolved using the Table View and Model View to validate structure and relationships.

I started by replacing missing or null values, standardizing entries across key columns such as job titles, programming languages, and salary bands. Columns containing compound information (e.g., salary range, job title & company) were split using delimiters to extract meaningful and usable data. In addition, I removed duplicates, renamed columns for clarity, and used functions to clean categorical data like trimming whitespaces and fixing inconsistent text casing.

To derive insights, I also created custom calculated columns such as the average salary by converting salary ranges into numeric estimates. I ensured data types were correctly set, and irrelevant or redundant columns were removed. These preprocessing steps laid a strong foundation for building interactive visualizations.

Visualizations & Insights

After preparing the data, I used Power BI's powerful visualization features to create an interactive dashboard that provides valuable insights into the data profession landscape. Charts and graphs were selected based on the nature of data and the story I wanted to tell.

A Stacked Bar Chart was used to display the average salary by job title, clearly highlighting how roles such as Data Scientist, Data Analyst, and Data Engineer differ in compensation. To explore trends in satisfaction, a Gauge illustrated how happy professionals are with their salary across different roles, with color-coded segments showing varied levels of satisfaction.

The Stacked Column Chart showed the favorite programming languages among data professionals, helping visualize popularity proportions for Python, SQL, R, and others. Slicersand interactive filters were added to allow users to explore results by region, experience level, or job title. By placing fields in areas like X-axis, Y-axis,Values, Legend, and Tooltips, I ensured each visual conveyed a clear, meaningful message.

Conclusion

This Power BI project not only enhanced my skills in data cleaning, data modeling, and interactive visualization, but also gave me hands-on experience with transforming raw data into meaningful business insights. By leveraging tools like the Power Query Editor, Table View, and Model View, I built a well-structured dataset and designed a dynamic dashboard that highlights key trends in the data profession. This project allowed me to apply my analytical thinking, attention to detail, and creativity—all of which are essential qualities for a data analyst. I'm proud of how this project turned out and excited to apply these skills in real-world scenarios.