Skip to content

Yogesh-F1/sql_data-analytics-project

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

34 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

sql-data-analytics-project 📊

A comprehensive collection of SQL scripts for data exploration, analytics, and reporting. These scripts cover various analyses such as database exploration, measures and metrics, time-based trends, cumulative analytics, segmentation, and more.

🔍 Quick Outline

Purpose — Ready-to-use, well-documented SQL scripts for common analytics tasks.

Structure — Two‑page deliverable (cover poster + content page) and categorized SQL scripts.

Audience — Data analysts, BI engineers, and learners seeking practical SQL examples.

📁 What's Included

SQL script collection — Organized by analytical theme:

  • Database Exploration — Inspect tables, columns, data types; DISTINCT and profiling queries.

  • Measures & Metrics — SUM, AVG, COUNT, and big-number checks.

  • Time-based Trends — Aggregations by day/month/year, MIN/MAX, DATEDIFF, seasonality checks.

  • Cumulative Analytics — Running totals, moving averages, and window function examples.

  • Segmentation & Reporting — CASE WHEN buckets, Top N reports, part-to-whole calculations.

Poster placeholder — High-quality poster layout integrated into the cover page for easy replacement.

README and LICENSE — This README plus a suggested license for reuse and attribution.

📚 Key Topics Covered

  • Dimensions & Measures — Distinguish categorical vs numeric fields; choose correct aggregations.

  • Trends & Time Analysis — Detect patterns, compute period-over-period metrics, and use date functions.

  • Performance Metrics — Compare current values to targets and historical baselines.

  • Cumulative & Windowing — Implement running totals, moving averages, and windowed comparisons.

  • Segmentation & Proportions — Create buckets, compute part-to-whole percentages, and produce Top N reports.

  • SQL Best Practices — Clear examples of DISTINCT, GROUP BY, CASE WHEN, subqueries, and window functions.

🧾 Credits & License

Attribution — Created by Data with Baraa — Baraa Khatib Salkini (YouTube: Data with Baraa). Please retain the credit line on the cover and footer when sharing publicly. 🙏

Suggested license — Consider a permissive license for sharing and reuse (e.g., MIT or CC BY 4.0). Add a LICENSE file to the repository to clarify reuse terms.

About

sql data analytics project for exploring and analyzing relational data—check tables, calculate metrics, spot trends, and create segments. Ideal for analysts and BI users: run the examples, learn the patterns, and adapt queries to your own database.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages