4 min to read
World Profession Survey
Explores various elements of the data profession, such as salary ranges, job satisfaction, and career changes among individuals working in data-related roles.
Overview
This project analyzes survey data related to the data industry, focusing on various aspects such as job titles, salaries, programming languages, and job satisfaction. The dataset includes responses from individuals in the data field, providing insights into their roles, experiences, and preferences.
Click this link to directly access the report:
Business Question
The primary business questions addressed in this project are:
- What is the average salary for different job titles in the data industry?
- What is the distribution of ethnicities among survey respondents?
- Which programming language is most preferred by data professionals?
- How does salary correlate with job satisfaction?
- How satisfied are individuals with their work-life balance?
- How difficult do individuals find breaking into the data field?
- What are the salary differences between genders in the data industry?
- How common is it for individuals to switch careers into data?
Project Structure
The project is organized as follows:
- Data Analysis: Exploration and analysis of the dataset to address business questions.
- Visualizations: Creation of charts and graphs to illustrate key findings.
- Reporting: Summarization of results and insights from the data.
Column Descriptions
The dataset contains the following columns:
- Unique ID: Unique identifier for each respondent.
- Email: Email address of the respondent (anonymized).
- Date Taken (America/New_York): Date when the survey was completed.
- Time Taken (America/New_York): Time when the survey was completed.
- Browser: Browser used to complete the survey.
- OS: Operating system used to complete the survey.
- City: City of the respondent.
- Country: Country of the respondent.
- Referrer: Source from which the respondent accessed the survey.
- Time Spent: Time spent on the survey.
- Q1 - Which Title Best Fits your Current Role?: Job title of the respondent.
- Q2 - Did you switch careers into Data?: Whether the respondent switched to a data career.
- Q3 - Current Yearly Salary (in USD): Respondent’s current yearly salary.
- Q4 - What Industry do you work in?: Industry of the respondent.
- Q5 - Favorite Programming Language: Respondent’s favorite programming language.
- Q6 - How Happy are you in your Current Position with the following?: Satisfaction ratings in various aspects (Salary, Work/Life Balance, Coworkers, Management, Upward Mobility, Learning New Things).
- Q7 - How difficult was it for you to break into Data?: Difficulty level of entering the data field.
- Q8 - If you were to look for a new job today, what would be the most important thing to you?: Most important job factor for the respondent.
- Q9 - Male/Female?: Gender of the respondent.
- Q10 - Current Age: Age of the respondent.
- Q11 - Which Country do you live in?: Country of residence.
- Q12 - Highest Level of Education: Highest level of education attained.
- Q13 - Ethnicity: Ethnicity of the respondent.
Workflow
- Data Cleaning: Handle missing values, format columns, and prepare the dataset for analysis.
- Exploratory Data Analysis (EDA): Examine the dataset to understand its structure and identify patterns.
- Analysis: Address business questions using statistical methods and visualizations.
- Visualization: Create charts and graphs to illustrate key findings.
- Reporting: Summarize results and insights in a report.
Exploring the Data
Exploratory Data Analysis includes:
- Summary Statistics: Compute basic statistics such as mean, median, and mode.
- Distribution Analysis: Analyze the distribution of key variables like salary and job satisfaction.
- Correlation Analysis: Investigate correlations between different variables.
Analyzing Trends
Analyze trends in the data by:
- Salary Analysis: Calculate average salaries by job title and gender.
- Programming Language Preferences: Determine the most popular programming languages.
- Happiness Metrics: Examine satisfaction with salary, work-life balance, and other aspects.
- Career Switch Trends: Assess how often individuals switch careers into data.
Visualizations
Key visualizations include:
- Average Salary by Job Title: Bar chart showing average salaries for different job titles.
- Count of Ethnicities: Pie chart displaying the distribution of ethnicities.
- Most Favorite Programming Language: Bar chart showing the most popular programming languages.
- Salary Happiness Diagram: Scatter plot illustrating the relationship between salary and job satisfaction.
- Work-Life Balance Happiness Diagram: Bar chart showing satisfaction with work-life balance.
- Job Difficulty: Bar chart representing the difficulty of breaking into the data field.
- Salary by Gender: Box plot comparing salaries between genders.
- Career Switch Observation: Pie chart showing the proportion of individuals who switched careers into data.
Reporting/Conclusion
The analysis provides insights into salary trends, job satisfaction, and career dynamics in the data field. Key findings include:
- Salary Trends: Variations in average salaries by job title and gender.
- Job Satisfaction: Correlation between salary and job satisfaction.
- Career Dynamics: Frequency of career switches into the data field and the challenges faced.
Source
The dataset used in this project can be found at the following link : Profession Survey.pdf
The dataset used in this project can be found at the following link: World Profession Survey Dataset
