A data visualization project
Data Analyst Job Market Analysis Using PostgreSQL
ROLE
Data Analyst
EXPERTISE
Data Analysis
YEAR
2024
This project involved creating actionable insights into the data analyst job market using PostgreSQL, following a hands-on tutorial by Luke Barousse. The analysis focused on identifying top-paying roles, high-demand skills, and skills associated with higher salaries.
Leveraging SQL and PostgreSQL, I explored job market trends, uncovering critical data patterns. The project was guided by Barousse’s course material and emphasized honing my SQL querying and analytical thinking skills.
Research & Setup
Studied course content to understand the dataset and define key questions. Connected PostgreSQL to execute queries on job market data.
Design & Implementation
Crafted SQL queries to extract insights on top-paying roles, in-demand skills, and salary trends. Analyzed the relationship between skills and compensation.
Insights & Visualization
Summarized findings into actionable insights using tables and graphs generated via SQL. Shared results using GitHub for version control and documentation.
Key Insights
Top-Paying Roles: Salaries for data analysts vary widely, with top remote roles offering up to $650,000/year.
In-Demand Skills: SQL, Python, and Tableau are essential for lucrative and high-demand roles.
Optimal Skills to Learn: Combining SQL, cloud tools (e.g., Snowflake, BigQuery), and business intelligence tools maximizes market value.
Tools Mastery
Strengthened SQL proficiency, including complex queries and data aggregation techniques.
Gained familiarity with PostgreSQL and analytical problem-solving approaches.
This project deepened my SQL expertise and provided actionable insights into the evolving data analytics landscape. By following Luke Barousse’s practical framework, I identified key trends in skills and salaries, enabling better decision-making for skill development.
This case study showcases my ability to extract valuable insights from data, reinforcing my readiness to contribute as a data analyst.