🧠 Data Science Jobs Analysis

This project analyzes data science job salaries using SQL and Python. It involves data wrangling, exploratory data analysis, and deriving insights from a dataset of job listings.

📁 Project Structure

📊 Dataset

The dataset used is ds_salaries.csv, which contains salary and job-related details for various data science roles worldwide.

Columns include:

• work_year
• experience_level
• employment_type
• job_title
• salary
• salary_in_usd
• employee_residence
• remote_ratio
• company_location
• company_size

🔧 Tools & Technologies

• SQL: Used to extract and aggregate job salary insights.
• Python (Pandas, Matplotlib): For data wrangling and visualization.
• Jupyter Notebook: To present the Python analysis interactively.
• PDF: Final report exported from the notebook for presentation.

📌 Key Tasks

1. Data Wrangling

   • Cleaned and prepared the data in wrangle_act.ipynb.
   • Converted salary to USD and checked for inconsistencies.

2. Exploratory Data Analysis

   • Investigated trends in job titles, salaries by experience level, and remote work patterns.

3. SQL Queries

   • Used in main.sql to query job data and extract insights like:
     • Highest paying roles
     • Most common job titles
     • Average salaries by location and company size

4. Insights

   • Documented in ds_salaries.pdf. Highlights include:
     • Remote roles often have higher average salaries.
     • Senior-level positions dominate the high salary range.
     • Most jobs are from the US and large companies.

📈 Sample Visuals

• Bar charts showing salary vs job title
• Pie charts for job distribution by country
• Box plots for salary variation by experience

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/ahmedshekooo/Ds_jobs.git
   cd Ds_jobs