Global Developer Trends Analysis (2026)

IBM Data Science Professional Capstone

An end-to-end data pipeline analyzing the evolution of developer skills, salary correlations, and the shift toward Cloud-native ecosystems.

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Dashboard Preview

Current Tech Usage	Future Trends

Annual Average Salary	Demographics	Job Postings

View Live Interactive Dashboard (IBM Cognos)

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Key Insights

• Core Stability: Python, SQL, and JavaScript remain the primary "Entry-to-Industry" stack for 2026.
• Infrastructure Pivot: 40% YoY growth in Cloud-native and NoSQL adoption vs. traditional RDBMS.
• Premium Stack: Go/Rust paired with Cloud Architecture outperforms standard Full-Stack roles by 18% in compensation.

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Data Pipeline & Tech Stack

Stage	Tools
Data Acquisition	BeautifulSoup (web scraping), REST APIs (job postings)
Processing	Python — Pandas, NumPy (70K+ records)
Analysis	SQL — SQLite
Visualization	PowerBI, IBM Cognos Analytics

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Getting Started

Requirements: Python 3.8+

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook

Run notebooks in this order:

1. Data Collections/ — data acquisition (APIs + web scraping)
2. EDA/Lab_00_DataWrangling_Pipeline.ipynb — full cleaning and wrangling pipeline
3. EDA/Lab_07_Removing_Duplicates.ipynb → Lab_08 → Lab_09 → Lab_10 → Lab_12 — focused EDA labs
4. Visualizations/ — charts and plots

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Repository Structure

Developer_skills_Trends_analysis/
├── Dashboard/          # IBM Cognos dashboard exports (PNG)
├── Data Collections/   # Acquisition notebooks (APIs, web scraping)
├── EDA/                # Exploratory data analysis notebooks
├── Visualizations/     # Chart notebooks
├── Presentation/       # Final presentation materials
└── data/
    └── processed/      # Cleaned CSVs used in analysis

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Data Sources

• Stack Overflow Annual Developer Survey
• Job posting data via REST APIs
• Compensation data via web scraping

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IBM Data Science Professional Certificate — Capstone Project