Data Professional Survey

Introduction

The data survey profession project aims to analyze demographics and sentiments within the data profession. This documentation focuses on the data-cleaning steps undertaken to ensure the quality and reliability of the dataset.

Objectives

The primary objective of the data cleaning project is to prepare a clean and reliable dataset for subsequent analysis. Clean data is crucial for obtaining accurate insights into the demographics and sentiments of professionals in the data survey.

Source

Alex the Analyst

Dataset Overview

The dataset consists of the following columns:

• Unique ID
• Email
• Date Taken (America/New_York)
• Time Taken (America/New_York)
• Browser
• OS
• City
• Country
• Referrer
• Time Spent
• Q1 - Which Title Best Fits Your Current Role?
• Q2 - Did you switch careers into Data?
• Q3 - Current Yearly Salary (in USD)
• Q4 - What Industry do you work in?
• Q5 - Favorite Programming Language
• Q6 - How Happy are you in your Current Position with the following? (Salary)
• Q6 - How Happy are you in your Current Position with the following? (Work/Life Balance)
• Q6 - How Happy are you in your Current Position with the following? (Coworkers)
• Q6 - How Happy are you in your Current Position with the following? (Management)
• Q6 - How Happy are you in your Current Position with the following? (Upward Mobility)
• Q6 - How Happy are you in your Current Position with the following? (Learning New Things)
• Q7 - How difficult was it for you to break into Data?
• Q8 - If you were to look for a new job today, what would be the most important thing to you?
• Q9 - Male/Female?
• Q10 - Current Age
• Q11 - Which Country do you live in?
• Q12 - Highest Level of Education
• Q13 - Ethnicity

Tools Used

• MS Excel
• Power BI

Data Cleaning Process

Data import

• Downloaded the dataset, had an overview of the CSV file on Excel, and imported the Dataset to Power BI

Filter Desired demographics

• Delete empty and non-used columns
• in column (Which Title Best Fits Your Current Role) 'Split column' 'By Delimiter' using 'Custom' '(' 'split at' 'left-most Delimiter' 'OK'. This is done so as to create a separate column to have the desired demographics, And then delete the new column.
• Repeat the previous step in column (What Industry do you work in)
• Repeat the previous step in column (Which Country do you live in)
• Repeat the previous step in column (Favorite Programming Language) but using 'colon' split at 'left-most Delimiter
• Make a new column to calculate the Average salary using (Current Yearly Salary (in USD))

Visualization

Conclusion

In conclusion, the insights gained from this analysis show:

• 630 people participated in this survey, with 468 Males and 162 Females.
• The average age of survey takers is 30 years.
• Data scientist makes the most income among all the other Data professionals and has the best work/life balance.
• People in the United States participated most with a survey count of 261, they have the most happiness and work/life balance and the best salary. This means The United States is the best country for any Data professional to work.
• Canada has the lowest survey count.
• India has the lowest salary, and also the lowest happiness and work/life balance, though that's where you will find the younger Data professionals.
• The most popular programming language is Python and the least is Java.
• Data Analyst makes the most use of all the programming languages.
• The Average salary of females is 55k while the male is 53k.