dannydave/sql-data-analytics-project
This repository contains a collection of SQL scripts demonstrating various analytical techniques, such as changes over time, cumulative, performance, data segmentation, part-to-whole analysis.
📊 Exploratory Data Analytics Project
A structured collection of SQL scripts designed for both Exploratory Data Analysis (EDA) and Advanced Analytics within relational databases.
This repository is organized into analytical themes, providing reusable, well-documented SQL queries to help data professionals quickly explore, segment, and analyze data while following SQL best practices.
📌 Table of Contents
📍 Project Overview
This project contains SQL templates and examples for:
- Quick data exploration
- Business performance tracking
- Trend analysis
- Data segmentation and reporting
The goal is to save time, promote SQL best practices, and make analysis more efficient.
🔍 Exploratory Data Analysis (EDA)
Gain a clear understanding of your database structure, content, and key metrics before deeper analysis.
- Database Exploration – Inspect schemas, tables, and relationships.
- Dimensions Exploration – Analyze categorical fields for distribution and uniqueness.
- Date Exploration – Identify time-based patterns, seasonality, and data completeness.
- Measures Exploration – Summarize numerical metrics (totals, averages, extremes).
- Magnitude Analysis – Assess the scale of measures to guide aggregation and visualization.
- Ranking – Identify top/bottom N entities (e.g., products, customers).
📈 Advanced Analytics
Perform deeper analysis to uncover trends, performance patterns, and actionable insights.
- Change-Over-Time Trends – Measure growth, decline, and rate of change.
- Cumulative Analysis – Compute running totals, cumulative percentages, and progressive performance.
- Performance Analysis – Compare metrics against benchmarks, targets, or historical data.
- Part-to-Whole Analysis – Evaluate category contributions to overall totals.
- Data Segmentation – Group data into cohorts or segments for targeted insights.
- Reporting Queries – Create output datasets for dashboards and BI tools.
🎯 Who This Is For
- Data Analysts – Ready-to-use SQL templates for frequent analysis needs.
- BI Developers – Reusable queries for dashboards and reporting pipelines.
- Data Scientists – A fast EDA toolkit before moving into modeling.
💡 Key Benefits
- Modular, topic-based structure for easy adaptation.
- Clean, well-commented SQL scripts that follow best practices.
- Covers both quick exploration and deep business analysis.
📜 License
MIT — see the LICENSE file.
🌟 About Me
I’m Daniel Toluwani Adeleke, a Data Scientist & IT professional with a passion for building end-to-end data solutions.
I hold a BSc in Computer Science and an MSc in Data Science & Business Analytics. My expertise includes SQL, Python, Machine Learning, and BI reporting.
📧 Email: dannydave1000@gmail.com
💼 LinkedIn: linkedin.com/in/dannydave
🌐 Portfolio: dannydave.my_portfolio.github.io