Data Professionals Survey Dashboard (630 Responses)

🔹 Project Overview

This project analyzes a dataset of 630 responses collected from a YouTuber’s survey targeting data professionals. The goal was to understand the career landscape, salaries, job satisfaction, and demographics of people working in data-related fields.

🔹 Objectives

Identify common job roles in the data industry

Compare average salaries across roles, experience, and demographics

Measure job satisfaction & happiness levels

Highlight trends in programming language usage

Explore demographics such as age and gender distribution

🔹 Data Source & Preparation

Source: YouTuber survey (630 participants)

Format: CSV file

Cleaning & Transformation:

Removed incomplete/null responses

Standardized job titles and education fields

Created calculated columns for age groups and experience ranges

Used Power Query for data cleaning and DAX for measures

🔹 Dashboard Features

Interactive filters (job role, gender, age group)
Salary vs Role analysis
Programming language preference distribution
Happiness index visualization (work-life balance, coworkers, learning, management, salary)
Demographic breakdown (gender ratio, average age)

(Include your dashboard screenshot here — like the one you shared)

🔹 Key Insights

Demographics

Total 630 professionals, average age ~30 years

Male respondents: 74%, Female: 26%

Job Roles & Salaries

Highest paid: Data Scientists (~94), Data Architects (~84), Data Engineers (~65)

Lower paid: Data Analysts (~55), Students/Entry-level (~27)

Advanced technical roles are more financially rewarding

Programming Languages

Python is the most preferred language by a large margin

R, C/C++, Java, and JavaScript trail far behind

Job Satisfaction & Happiness (Scale ~10)

Work-life balance: 5.74

Coworker relationships: 5.61

Learning opportunities: 5.33

Management: 4.76

Salary: lowest at 4.27

Work-Life Balance

Average score of 5.74/10 → moderate satisfaction but room for improvement

🔹 Tools & Techniques Used

Power BI: Power Query, DAX, Data Modeling, Interactive Dashboard

Data Analysis: Categorical data analysis, KPI measurement, visualization

Visualization Techniques: Bar charts, pie charts, gauges, KPI cards

🔹 Outcome

The dashboard provides a clear snapshot of the data industry workforce based on real survey data. It helps learners, recruiters, and professionals understand:

Which roles dominate the industry

Salary expectations across positions

Levels of job satisfaction

Programming language trends

This project demonstrates my ability to analyze survey data, design interactive dashboards, and communicate insights effectively using Power BI.