A collection of data analytics projects built using Python, Pandas, Matplotlib, and Seaborn to explore real-world datasets and generate actionable insights through exploratory data analysis, visualization, and statistical investigation.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Data Visualization
- Statistical Analysis
- Data Transformation
- Business Intelligence
A comprehensive analysis of the U.S. data job market using real-world job posting data.
The project investigates:
- Skill demand across data roles
- Salary trends
- Hiring patterns
- Geographic distribution of jobs
- Optimal skills based on demand and compensation
- What does the overall data job market look like?
- Which skills are most demanded for major data roles?
- How do Data Analyst skills trend over time?
- Which skills command the highest salaries?
- Which skills offer the best balance of demand and compensation?
- SQL remains one of the most valuable foundational skills for Data Analysts.
- Python continues to show strong demand and salary potential.
- Specialized technical skills often command premium compensation.
- High demand does not always correspond to the highest salaries.
- The strongest career opportunities balance both demand and compensation.
- Python
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook
📂 Open: Data_Analyst_Job_Market_Analysis
Python_Projects_Data_Analytics
│
├── Data_Analyst_Job_Market_Analysis
│ ├── 1_Data_Jobs_EDA.ipynb
│ ├── 2_Skill_Demand_Analysis.ipynb
│ ├── 3_Skill_Trend_Analysis.ipynb
│ ├── 4_Salary_and_Skill_Compensation_Analysis.ipynb
│ ├── 5_Optimal_Skills_Analysis.ipynb
│ └── README.md
│
└── README.md
These projects demonstrate practical data analytics workflows using Python, including data cleaning, exploratory analysis, visualization, trend analysis, salary analysis, and insight generation.
The techniques used throughout these projects are commonly applied in Data Analyst, Business Intelligence Analyst, and Reporting Analyst roles.