Data-Driven Decision Making in SQL 🚀

Advanced SQL project focused on data-driven decision making. Includes subqueries, window functions, OLAP, and predictive analysis using PostgreSQL.

Welcome to the Data-Driven Decision Making in SQL project repository! 📈 This project showcases advanced SQL techniques and analytical strategies used to support data-informed decisions. Built on the DataCamp course curriculum, the repository presents real-world business scenarios and technical solutions with PostgreSQL.

Project Overview 📄

This repository explores how SQL can be used to:

• Optimize business strategy and performance through data.
• Support operational and strategic decision-making with complex queries.
• Derive actionable insights using advanced SQL constructs.

Repository Structure 📂

Data-Driven-Decision-Making-in-SQL/
├── LICENSE
├── README.md
├── certificate/
│   ├── Data-Driven-Decision-Making-Certificate.png
│   └── README.md
├── data/
│   ├── README.md
│   └── erdiagram.png (if available)
├── docs/
│   ├── business-scenarios-and-subqueries.md
│   ├── exists-union-intersect.md
│   ├── olap-queries.md
│   ├── window-functions-and-partitioning.md
│   ├── advanced-aggregation-and-grouping.md
│   ├── predictive-analytics-with-sql.md
│   └── README.md
├── sql/
│   ├── 01_Strategic_Subqueries_and_Indexing.sql
│   ├── 02_Advanced_Joins_and_Union.sql
│   ├── 03_OLAP_and_Window_Functions.sql
│   ├── 04_Predictive_Analytics_and_Campaigns.sql
│   └── README.md
└── visuals/
    ├── README.md
    └── charts-and-insights.png

Key Concepts ✨

🔢 Strategic Subqueries & Filtering

• Subqueries for conditional logic.
• EXISTS for performance-focused filtering.
• Use of IN, NOT IN, and HAVING with GROUP BY.

🔀 Joins, Union & Intersect

• Multi-source data comparison using UNION and INTERSECT.
• Strategic filtering and segmentation across datasets.

⚖️ OLAP Queries & Window Functions

• Ranking customers or regions with RANK() and ROW_NUMBER().
• Generating multidimensional views using CUBE and ROLLUP.

📊 Predictive Analytics & Campaign Logic

• Conditional logic to identify qualified customers.
• Segmenting purchase patterns over time.
• Forecasting behavior using LEAD() and LAG() functions.

Real-World Examples 💡

• Identify profitable customer segments using EXISTS.
• Merge campaign participants across years using UNION.
• Find overlapping customers using INTERSECT.
• Rank customers by purchase value within regions using window functions.
• Forecast next likely purchase using LEAD().

Certification 🎓

A certificate of completion from DataCamp is available in /certificate.

Documentation 📖

Each major concept is documented in /docs to serve as a reference or training guide.

Data & Visuals 📉

• If applicable, ER diagrams and example visualizations are in the /data and /visuals folders.

License 📜

This repository is licensed under the MIT License.