{"id":51674440,"url":"https://github.com/belpa03/sql-data-science-job-salaries","last_synced_at":"2026-07-15T03:35:31.364Z","repository":{"id":318120687,"uuid":"1070059094","full_name":"belpa03/sql-data-science-job-salaries","owner":"belpa03","description":"SQL-based exploratory analysis on global data science salaries (Kaggle dataset). 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Data Cleaning \u0026 Filtering (NULL Check)  \nEnsures dataset integrity before analysis by checking for missing or null values. This step helps identify incomplete records and prevents skewed insights during salary analysis.\n\n---\n\n### 🔍 2. Job Title Exploration  \nAnalyzing all distinct job titles in the dataset reveals the diversity of roles within the data science field — from “Data Analyst” and “Data Engineer” to “Machine Learning Specialist.” This step helps understand how varied the industry landscape truly is.\n\n---\n\n### 💼 3. Focus on “Data Analyst” Roles  \nFiltering the dataset to only include “Data Analyst” roles helps narrow the scope of analysis to a specific, comparable job category. This allows a fairer comparison of salaries and growth trends among professionals with similar responsibilities.\n\n---\n\n### 💰 4. Global Average Salary Insight  \nThe overall average annual salary in USD offers a benchmark to evaluate how competitive data-related roles are worldwide. It also sets the baseline for analyzing salary disparities between regions and experience levels.\n\n---\n\n### 💵 5. Salary Conversion to Local Currency  \nConverting the average salary to monthly income in Indonesian Rupiah (IDR) provides a more relatable perspective for local audiences. This highlights the earning gap between global standards and domestic opportunities.\n\n---\n\n### 📊 6. Experience-Level Salary Comparison  \nComparing salaries across experience levels (Entry vs Mid) reveals clear patterns of compensation growth. Typically, mid-level professionals earn nearly double entry-level salaries, indicating a strong return on experience.\n\n---\n\n### 🚀 7. Salary Growth Over Time  \nTracking salary growth for mid-level and experienced analysts across years shows industry trends — such as the post-pandemic salary spikes in tech and data analytics roles, followed by stabilization after 2022.\n\n---\n\n### 🌍 8. Geographic Salary Variation  \nSalaries differ greatly by company location. Analysts based in the U.S. or Western Europe earn significantly higher salaries than those in developing regions, underscoring the global economic imbalance in tech compensation.\n\n---\n\n### 🏠 9. Remote Work \u0026 Compensation Impact  \nRemote roles often provide higher pay for the same experience level, reflecting how global hiring allows companies to tap into broader talent pools and reward skill over geography.\n\n---\n\n### 🧩 10. Company Size vs Salary Distribution  \nLarger companies generally offer higher salaries, but mid-sized firms often show more balanced compensation growth and flexibility — suggesting that salary isn’t the only factor in career satisfaction.\n\n---\n\n## 🧠 SQL Theory Used  \n\nThis project utilizes several key SQL concepts to perform end-to-end data analysis and derive insights from the Data Science Salaries dataset:\n\n1. **Data Cleaning \u0026 Filtering (NULL Check)**  \n   Ensuring data integrity by detecting missing or null values before analysis.\n\n2. **Exploratory Data Analysis (EDA) with SQL**  \n   Using `SELECT`, `WHERE`, `DISTINCT`, and `ORDER BY` to explore job titles, salary patterns, and role distributions.\n\n3. **Aggregation \u0026 Grouping (AVG, GROUP BY, HAVING)**  \n   Calculating average salaries, grouping data by experience level and company location, and filtering aggregated results.\n\n4. **CTE (Common Table Expressions)**  \n   Simplifying complex analytical queries — particularly when comparing salary trends between experience levels over time.\n\n5. **Business Insight Interpretation**  \n   Translating analytical results into actionable insights for recruiters, job seekers, and HR professionals to understand salary dynamics in data-related roles.\n\n---\n\n💡 **Business Takeaways**\n\n- Experience is the strongest driver of salary growth.  \n- Remote flexibility boosts compensation and access to global jobs.  \n- Mid-sized firms are emerging as competitive employers in data.  \n- Global mobility reshapes salary fairness and job distribution.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbelpa03%2Fsql-data-science-job-salaries","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbelpa03%2Fsql-data-science-job-salaries","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbelpa03%2Fsql-data-science-job-salaries/lists"}