Data Analyst Career Deep Dive: Salary & Growth Analysis

Project Overview

Unlike general data science market analyses, this project provides a targeted deep dive into the Data Analyst career path. Using a real-world dataset (ds_salaries.csv), I simulated a relational database environment in Python to execute specific SQL queries aimed at answering critical career questions for aspiring analysts.

Key Objectives

• Role-Specific Analysis: Focusing strictly on "Data Analyst" job titles to avoid generalization bias.
• Salary Estimation (IDR): Converting and estimating monthly salaries in Indonesian Rupiah to provide local context.
• Career Progression: Measuring the financial leap from Mid-level to Executive positions.
• Global Hotspots: Identifying top countries for entry-level analysts.

Technical Implementation

This project utilizes a Hybrid Workflow:

1. SQL (SQLite): Used for all data manipulation, including:
   • CTE (Common Table Expressions) for calculating salary gaps.
   • GROUP BY & HAVING for regional analysis.
   • Data Cleaning (NULL checks).
2. Python (Pandas & Seaborn): Used for:
   • Database connection & ingestion.
   • Visualizing salary trends and distributions.

Key Findings

• Significant Growth: There is a substantial salary gap between Mid-level and Executive roles, confirming a high ceiling for career growth in this path.
• Global Opportunities: While the US dominates, specific countries offer competitive salaries >$20k/year even for Entry-level roles.
• Employment Type: Full-time (FT) roles provide the most stability and highest average compensation compared to contract work.

Files in This Repository

• Portfolio_V2_DeepDive_Analyst.ipynb: The main notebook containing the code, queries, and visualizations.
• ds_salaries.csv: The dataset used for analysis.
• Script Modul 11.sql: Reference SQL script containing the original business questions.

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Created by Subki as part of Data Science Portfolio.