Portfolio Project | Python | SQL | Power BI | Data Analytics
Python · MySQL · Power BI | 6 Datasets · 14 Countries · 8 Industries · 517 Rows
An end-to-end data analytics project tracking how AI has evolved globally across industries, geographies, and cloud platforms from 2019 to 2025.
Built a complete pipeline — raw data ingestion, cleaning, EDA, SQL storage, and a 5-page interactive Power BI dashboard — to answer three business questions:
- Which industries and regions lead AI adoption, and which lag?
- Where does high AI readiness meet low market revenue (opportunity gap)?
- How are AWS, Azure and Google Cloud competing for dominance?
Project Architecture
Raw Data Sources ↓ Python Data Cleaning (pandas) ↓ Merged Analytical Tables ↓ MySQL Database ↓ SQL Analytical Queries ↓ Power BI Dashboard ↓ Project_documentation
| Python | pandas, SQLAlchemy — data cleaning, EDA, multi-source merging, MySQL upload |
| MySQL | Window functions, CAGR calculations, CASE WHEN tiers, aggregation queries |
| Power BI | DAX measures, matrix heatmap, scatter quadrant, forecast line, interactive slicers |
6 industry datasets merged into 3 analytical tables:
| Source | What It Covers |
|---|---|
| McKinsey State of AI | Industry adoption rates by region (2019–2025) |
| Stanford HAI Index | Country-level AI investment, patents, research |
| OECD AI Observatory | Policy count, readiness scores, talent scores |
| Statista | Market revenue and CAGR by country |
| IDC | Enterprise spending, use cases, scaling barriers |
| Amazon / Alphabet Annual Reports | AWS, Azure, GCP cloud revenue and market share |
Global AI adoption rose from 33.9% → 73.4% between 2019 and 2025. Total market revenue reached $715.7B. Only 45% of AI pilots reach production — a scaling gap visible across all industries.
USA leads with $326.8B in total investment — 5× China. North America holds 55.5% of global AI revenue. India improved AI readiness faster than any country: +15.8 points in 5 years.
Technology leads at 68% average adoption. Healthcare lags at 35% — Regulatory Compliance is its dominant barrier. Talent Gap is the #1 scaling barrier across all 8 industries.
AWS leads in revenue ($107.6B) but losing market share (33% → 30%). Azure growing fastest at 44.3% avg YoY — Microsoft's OpenAI investment visible as a direct inflection point in 2023 data.
7 countries — Singapore, Australia, Japan, Canada, South Korea, Germany, France — show AI readiness above 80 but underperforming market revenue. Projected 2025 global revenue: $401B (52.2% CAGR basis).
- AI adoption doubled globally in 6 years — every region accelerated post-2021
- USA dominates investment but India and Brazil are closing the readiness gap faster than raw numbers suggest
- Azure is the cloud growth story — fastest-growing provider, OpenAI partnership directly reflected in data
- Healthcare is the biggest scaling laggard — regulatory compliance, not talent or data, is the primary barrier
- 7 high-readiness countries are under-monetised — the clearest vendor opportunity signal in the dataset
# Install dependencies
pip install pandas sqlalchemy mysql-connector-python
# Set your MySQL password and CSV folder path in the script (2 lines)
# Then run:
python AI_Adoption_Analysis.pyThis auto-creates the database, cleans all data, runs EDA, and uploads 3 tables to MySQL.
Open MySQL Workbench → run queries from AI_adoption_Analysis_sql_queries.txt
Load 3 clean CSVs from data/clean/ into Power BI.
Global-AI-Adoption-Market-Landscape-2019-2025
│
├── Raw data from all sources
│ └── Raw AI Adoption Datasets All6.zip
│ # 6 original source CSV datasets
│
├── Python code
│ └── AI Adoption Analysis code.py
│ # Full Python pipeline: data cleaning, EDA, merging, MySQL upload
│
├── Cleaned files (Python Output)
│ ├── ai_cloud.csv
│ ├── ai_geo.csv
│ └── ai_industry.csv
│ # 3 merged analytical tables generated by Python
│
├── SQL Queries
│ └── All sql queries with questions and expected answers.sql
│ # 20 analytical queries using MySQL
│
├── Power BI Dashboard
│ └── AI-Adoption-Dashboard.pbix
│ # 6-page interactive dashboard
│
└── Dashboard Snaps
│ └── Dashboard screenshots
│ # Images used in README preview
│
└──Project Documentation
└──AI_Adoption_Project_Summary.pdf
Data: McKinsey · Stanford HAI · OECD · Statista · IDC · Amazon / Alphabet Annual Reports · till 2025
Data Analytics Project
Data Analyst Portfolio
Python Data Analysis
SQL Data Analysis
Power BI Dashboard Project
Business Intelligence Dashboard
AI Market Analysis
End-to-End Data Analytics Pipeline
Data Cleaning with Python
Data Visualization with Power BI