The objective of this project was to analyze global data careers salaries to understand how factors such as job role, experience level, employment type, remote flexibility, and geographic location influence compensation patterns in the data industry.

A comprehensive analysis of data science job roles and global salary trends, combining exploratory data analysis with machine learning based salary range prediction.

Exploratory data analysis of salary survey data to identify compensation patterns and relationships between salary, experience, location, and job roles.

This project analyzes global salary patterns across Data Science and AI roles to inform hiring strategy, cost optimization, and compensation risk planning.

Companies struggle to offer competitive salaries for data science-related positions. This project analyzes salary determinants, predicts expected compensation, segments job roles, and visualizes job families to improve HR compensation strategy and hiring decisions.

This project provides an in-depth market analysis of compensation for in-demand Artificial Intelligence (AI) and Data-focused roles across the European Union.

Exploratory and business-focused analysis of employee salary data to identify key compensation drivers such as experience, education, job role, and gender.

A Power BI dashboard project analyzing global salary trends across Data Science, AI, and other tech roles between 2020 and 2025. The dashboard explores how job title, experience, company size, location, and work mode impact compensation and employment trends.

The Ask a Manager Salary survey offers a unique opportunity to delve into real-world salary data, providing valuable insights into the complex dynamics of compensation across various industries and job roles. This analysis aim to uncover and explore compensation patterns in United States specifically within the Ask a Manager Salary Survey 2021.

An interactive HR Analytics dashboard built in Microsoft Power BI to analyze workforce distribution, compensation trends, job roles, department performance, employee demographics, and work-life balance. Includes Drillthrough, Tooltips, Filters, and dynamic visuals for deep insights into employee data.

This repository contains a Power BI project analyzing salaries for IT and Data Science roles across five countries: Canada, USA, UK, Australia, and China. The project aims to provide insights into salary trends, regional differences, and factors influencing compensation in the tech industry.

This project leverages historical salary data in the **AI industry** to predict future compensation trends using machine learning. With salary records from 2019 to 2024, the model forecasts expected salaries for AI-related roles for the years 2025, 2026, and 2027.

This Power BI project analyzes real IT salary data from 2020 to 2023, revealing trends based on job title, experience level, company size, and employee location. Key insights include top-paying roles, salary growth by experience, and the evolution of total compensation over time.

This project is my first venture into using Power BI for analyzing a real-life dataset, focusing on job titles and salaries in the data science industry. The primary objective was to explore the current trends in job roles, understand the salary distributions, and identify key factors influencing compensation in this field.

Analyzed employee compensation data to explore salary distribution, workforce composition, and role-based patterns across locations and technologies. Applied statistical techniques including normalization and hypothesis testing (e.g., Z-test, T-test) to validate business assumptions. Used Python libraries and ML to develop visual and quantitative i

The Salary Prediction project aims to build a machine learning model that can predict an individual's salary based on various input features such as education level, years of experience, job role, and more. The goal is to help individuals, companies, or HR teams make better data-driven decisions regarding compensation.

Problem Statement: Every year a lot of companies hire a number of employees. The companies invest time and money in training those employees, not just this but there are training programs within the companies for their existing employees as well. The aim of these programs is to increase the effectiveness of their employees. But where HR Analytics fit in this? and is it just about improving the performance of employees? HR Analytics Human resource analytics (HR analytics) is an area in the field of analytics that refers to applying analytic processes to the human resource department of an organization in the hope of improving employee performance and therefore getting a better return on investment. HR analytics does not just deal with gathering data on employee efficiency. Instead, it aims to provide insight into each process by gathering data and then using it to make relevant decisions about how to improve these processes. Attrition in HR Attrition in human resources refers to the gradual loss of employees overtime. In general, relatively high attrition is problematic for companies. HR professionals often assume a leadership role in designing company compensation programs, work culture, and motivation systems that help the organization retain top employees. How does Attrition affect companies? and how does HR Analytics help in analyzing attrition? We will discuss the first question here and for the second question, we will write the code and try to understand the process step by step. Attrition affecting Companies A major problem in high employee attrition is its cost to an organization. Job postings, hiring processes, paperwork, and new hire training are some of the common expenses of losing employees and replacing them. Additionally, regular employee turnover prohibits your organization from increasing its collective knowledge base and experience over time. This is especially concerning if your business is customer-facing, as customers often prefer to interact with familiar people. Errors and issues are more likely if you constantly have new workers.

Explore our GitHub repo for a comprehensive employee salary prediction project. Using ML, we predict salaries based on experience, education, and job roles. Discover data preprocessing, analysis, model selection, and training for insights into compensation management.

Test code, vibecoded Canadian Compensation Data for tech companies and 50+ roles

Analyze employee salary data to understand compensation patterns across roles and departments.

ombining live API data, Kaggle CSVs, and salary benchmarks to analyze compensation across data roles in the US.

Analyzed remote Data Analyst job postings to study how skill demand and compensation vary across roles.

Tableau-based data visualization and analysis of public-sector salary data, uncovering workforce pay trends, top roles, and compensation structures.

Exploratory Data Analysis (EDA) of employee salary data to uncover patterns in compensation and job roles using Python, Pandas, and Matplotlib.

Executive sales job market intelligence for VP Sales and CRO roles. Weekly hiring trends, compensation data, and career insights.

Interactive Power BI dashboard visualizing global data roles, salaries, and work-life balance metrics for strategic compensation modelling.

This project analyzes global salary data across data and AI roles to benchmark market compensation patterns and identify pay differences by role, experience level, and company size..

Salary analysis of global data job market using SQL to explore compensation patterns across roles, experience levels, and locations

Which skills actually pay in data jobs? A tiered SQL analysis segmenting Data Analyst and Data Scientist roles into compensation tiers to find out.

An interactive dashboard presenting key insights and trends in compensation across various data roles, helping to compare salaries and identify patterns.