https://github.com/belpa03/sql-data-science-job-salaries
SQL-based exploratory analysis on global data science salaries (Kaggle dataset). Includes data cleaning, EDA, aggregation, and business insights highlighting experience, company size, and remote work impact.
https://github.com/belpa03/sql-data-science-job-salaries
business career-trends data data-analysis datacleaning eda mysql salary-analysis sql
Last synced: about 10 hours ago
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SQL-based exploratory analysis on global data science salaries (Kaggle dataset). Includes data cleaning, EDA, aggregation, and business insights highlighting experience, company size, and remote work impact.
- Host: GitHub
- URL: https://github.com/belpa03/sql-data-science-job-salaries
- Owner: belpa03
- Created: 2025-10-05T07:11:50.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-05T07:18:54.000Z (9 months ago)
- Last Synced: 2025-10-05T09:09:51.983Z (9 months ago)
- Topics: business, career-trends, data, data-analysis, datacleaning, eda, mysql, salary-analysis, sql
- Homepage:
- Size: 6.84 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 💼 Data Science Salaries Analysis — SQL Project
## đź“– Project Overview
This project performs an **in-depth SQL analysis** on the [Data Science Job Salaries dataset](https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries).
Developed as part of a *SQL certification class* and further expanded into actionable **business insights** that can help recruiters and data professionals understand salary dynamics across experience levels, job titles, and company types.
---
## ⚙️ Tools & Skills Used
- **SQL (MySQL)** — Data Cleaning, Filtering, Aggregation, and Advanced Querying
- **CTE (Common Table Expression)** — For multi-level salary comparison
- **Data Interpretation** — Translating numbers into business insights
- **Business Understanding** — Identifying career trends in data professions
---
## 🧩 Insights
### 🧹 1. Data Cleaning & Filtering (NULL Check)
Ensures dataset integrity before analysis by checking for missing or null values. This step helps identify incomplete records and prevents skewed insights during salary analysis.
---
### 🔍 2. Job Title Exploration
Analyzing 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.
---
### 💼 3. Focus on “Data Analyst” Roles
Filtering 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.
---
### đź’° 4. Global Average Salary Insight
The 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.
---
### đź’µ 5. Salary Conversion to Local Currency
Converting 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.
---
### đź“Š 6. Experience-Level Salary Comparison
Comparing 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.
---
### 🚀 7. Salary Growth Over Time
Tracking 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.
---
### 🌍 8. Geographic Salary Variation
Salaries 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.
---
### 🏠9. Remote Work & Compensation Impact
Remote 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.
---
### 🧩 10. Company Size vs Salary Distribution
Larger 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.
---
## 🧠SQL Theory Used
This project utilizes several key SQL concepts to perform end-to-end data analysis and derive insights from the Data Science Salaries dataset:
1. **Data Cleaning & Filtering (NULL Check)**
Ensuring data integrity by detecting missing or null values before analysis.
2. **Exploratory Data Analysis (EDA) with SQL**
Using `SELECT`, `WHERE`, `DISTINCT`, and `ORDER BY` to explore job titles, salary patterns, and role distributions.
3. **Aggregation & Grouping (AVG, GROUP BY, HAVING)**
Calculating average salaries, grouping data by experience level and company location, and filtering aggregated results.
4. **CTE (Common Table Expressions)**
Simplifying complex analytical queries — particularly when comparing salary trends between experience levels over time.
5. **Business Insight Interpretation**
Translating analytical results into actionable insights for recruiters, job seekers, and HR professionals to understand salary dynamics in data-related roles.
---
đź’ˇ **Business Takeaways**
- Experience is the strongest driver of salary growth.
- Remote flexibility boosts compensation and access to global jobs.
- Mid-sized firms are emerging as competitive employers in data.
- Global mobility reshapes salary fairness and job distribution.