📊 Tech Job Market Analysis

📌 Project Overview

This project analyzes trends in the tech job market, focusing on:

• Salary ranges across various tech roles
• Required skills & technologies for different job positions
• Prevalence of hybrid & remote jobs
• Salary and Location analysis
• Skills and Salary analysis
• Remote Jobs VS Onsite Jobs analysis

📂 Files and their Purposes:

• analysis_files contains jupyter notebook and the analysis for popular jobs
• joining_data contains CSV files that were considered in the analysis file
• separated_jobs contains CSV files that were scraped from Glassdoor and separated on job_roles.
• data_cleaning.ipynb we cleaned the collected data(converted per month, per hour salary into per year, removed unwanted characters and so on)
• data_extraction.ipynb contains code for extracting data from Glassdoor website
• separating_job_role.ipynb contains code for separating the data according to job roles

📂 Dataset

We will collect and analyze datasets related to tech jobs, sourced from:

• Online job portals (Glassdoor)
• Market reports & industry surveys

🚀 Methodology

1. Data Collection: Scraping and gathering relevant job data
2. Data Cleaning & Processing: Handling missing values, standardizing job titles, etc.
3. Exploratory Data Analysis (EDA): Visualizing trends and patterns
4. Insights & Reporting: Summarizing findings with visualizations

📊 Visualizations & Insights

• Bar charts for salary distributions across different tech roles
• Word clouds for most in-demand skills
• Pie charts for Remote VS Onsite jobs
• Swarm Chart for salary vs skills

🛠️ Technologies Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Data Scraping (BeautifulSoup, Selenium)
• Visualization (Matplotlib, Seaborn)

🎯 Goals

• Identify the best-paying tech jobs
• Determine the most in-demand skills
• Determine the locations with most job-postings
• Provide data-driven career insights

💡 Future Improvements

• Expand dataset with real-time job postings
• Incorporate more AI-based trend analysis
• Develop an interactive dashboard

🤝 Contributions

Open to collaboration! If you want to contribute, feel free to:

• Suggest new data sources 📂
• Improve predictive models 📈
• Enhance visualizations 🎨

📜 License

This project is open-source under the MIT License.

📢 Follow Us

Stay updated with our latest findings and insights!

• Rhythm Bhetwal:
• Nabin Koirala:

---

📌 Stay tuned for updates! 🚀