This project analyzes survey responses from 630 data professionals across various countries. It explores job roles, salaries, favorite programming languages, work/life balance, and the difficulty of entering the data field. The goal was to gain insights into current trends in the data profession and enhance my data analysis and visualization skills. 

Objectives

• To explore key trends in the data profession, including salary, job roles, and skill preferences.

• To identify challenges faced by newcomers entering the data field.

• To analyze overall job satisfaction based on work/life balance and salary.

• To improve data visualization and storytelling using real-world survey data.

Initial Situation:

There was limited clarity on how different roles in data compare in terms of satisfaction, skills, and salary. Many aspiring professionals are unsure what to expect when entering the field.

Goals and Considerations:

• Provide clear, data-driven insights for aspiring and current data professionals.

• Ensure visualizations are simple, accurate, and easy to interpret.

• Maintain fairness and accuracy in representing the diverse survey responses.

Results

• Python is the top preferred programming language across all job roles.

• Data Scientist is the highest-paying role among data professionals.

• Over 42% of participants found it difficult to break into the data field.

• Work/life balance satisfaction is moderate (5.74/10), while salary satisfaction is relatively low (4.27/10).

• Most survey respondents are from USA, India, UK, and Canada.

• Students and entry-level professionals reported the lowest salary levels.

[Github]

This HR Analytics Dashboard provides a detailed overview of employee attrition within an organization of 1,470 employees. It explores factors such as age, gender, education, salary, and job roles to identify trends in employee turnover. 

Objectives

• To analyze employee attrition patterns and understand key contributing factors.

• To identify high-risk groups and job roles with the highest turnover.

• To provide insights for HR teams to improve retention strategies.

Initial Situation

The organization had limited visibility into why employees were leaving. No clear data-driven strategy was in place to identify which segments were most affected by attrition.

Goals and Considerations

• Build a visual, interactive dashboard for quick decision-making.

• Focus on age, education, salary, and job roles for detailed insights.

• Ensure clarity and accessibility for both HR and management teams.

• Use color coding and simple charts for easy interpretation.

Results

• The overall attrition rate is 16.1% with 237 employees leaving.

• The age group 26–35 experienced the highest attrition (116 people).

• Life Sciences (41%) and Medical (32%) backgrounds had the highest turnover.

• Laboratory Technicians had the highest job role attrition (62), followed by Sales Executives (57) and Research Scientists (47).

• Males (150) had higher attrition than females (87).

• Most employees who left had salaries up to 5k and 7 average years of service.

[Github]

Write an introduction here for your project. Provide a brief overview, why you chose to pursue this project,  mention others involved, where the project was completed, and provide any credits needed.

Objectives

List the primary objectives for your project here. What was the initial situation? What did you seek to change or influence? What were the essential considerations throughout your process as you worked to achieve your goal?

Results

What were your findings or results? Were you able to make your desired impact or discover something along the way? Did anything unexpected or of note happen during your journey? Was there anything specific you learned? Will the results from this project impact anything else in the future?

[Github]