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📖 Background

You work for an international HR consultancy helping companies attract and retain top talent in the competitive tech industry. As part of your services, you provide clients with insights into industry salary trends to ensure they remain competitive in hiring and compensation practices.

Your team wants to use a data-driven approach to analyse how various factors—such as job role, experience level, remote work, and company size—impact salaries globally. By understanding these trends, you can advise clients on offering competitive packages to attract the best talent.

In this competition, you’ll explore and visualise salary data from thousands of employees worldwide. f you're tackling the advanced level, you'll go a step further—building predictive models to uncover key salary drivers and providing insights on how to enhance future data collection.

💾 The data

The data comes from a survey hosted by an HR consultancy, available in 'salaries.csv'.

Each row represents a single employee's salary record for a given year:
  • work_year - The year the salary was paid.
  • experience_level - Employee experience level:
    • EN: Entry-level / Junior
    • MI: Mid-level / Intermediate
    • SE: Senior / Expert
    • EX: Executive / Director
  • employment_type - Employment type:
    • PT: Part-time
    • FT: Full-time
    • CT: Contract
    • FL: Freelance
  • job_title - The job title during the year.
  • salary - Gross salary paid (in local currency).
  • salary_currency - Salary currency (ISO 4217 code).
  • salary_in_usd - Salary converted to USD using average yearly FX rate.
  • employee_residence - Employee's primary country of residence (ISO 3166 code).
  • remote_ratio - Percentage of remote work:
    • 0: No remote work (<20%)
    • 50: Hybrid (50%)
    • 100: Fully remote (>80%)
  • company_location - Employer's main office location (ISO 3166 code).
  • company_size - Company size:
    • S: Small (<50 employees)
    • M: Medium (50–250 employees)
    • L: Large (>250 employees)

💪 Competition challenge

In this first level, you’ll explore and summarise the dataset to understand its structure and key statistics. If you want to push yourself further, check out level two! Create a report that answers the following:

  • How many records are in the dataset, and what is the range of years covered?
  • What is the average salary (in USD) for Data Scientists and Data Engineers? Which role earns more on average?
  • How many full-time employees based in the US work 100% remotely?
import pandas as pd

# Load the CSV file
df = pd.read_csv('salaries.csv')
df

#Count the Number of Records

number_of_records = df.shape[0]
number_of_records
#Determine the Range of Years
start_year = df['work_year'].min()
end_year = df['work_year'].max()
start_year
end_year
#Output the Results:
print(f"Number of records: {number_of_records}")
print(f"Range of years covered: {start_year} to {end_year}")

Salary Data Analysis Report

1. Dataset Overview

  • Number of Records: {number of records}
  • Range of Years Covered: {start year} to {end year}

2. Average Salaries

  • Average Salary for Data Scientists: {average salary for Data Scientists in USD}
  • Average Salary for Data Engineers: {average salary for Data Engineers in USD}
  • Comparison: On average, {Data Scientist or Data Engineer} earns more.

3. Remote Work Statistics

  • Number of Full-Time Employees in the US Working Remotely: {number of full-time remote employees in the US}

Conclusion

This analysis provides insights into salary trends across various roles and the impact of remote work on compensation. By understanding these trends, companies can offer competitive packages to attract top talent in the tech industry.