📊 SQL Learning Portfolio

This repository documents my progress in learning SQL for Data Science and AI Engineering.
The goal is to master SQL fundamentals, practice on different platforms, and prepare for applying SQL in data pipelines, analytics, and ML/AI workflows.

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🚀 Learning Path and Progression

• Mode Analytics SQL Tutorial
  • ✅ Basic SQL (SELECT, WHERE, ORDER BY, LIMIT, operators) → done
  • ✅ Intermediate SQL (Joins) → done
  • ⏳ Advanced SQL (Window functions, Subqueries) → in progress
• Kaggle SQL Micro-Course → Applied SQL on real-world datasets → done
• LeetCode SQL Problems → Interview-style query practice (to be added).

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🎯 Goals

• Build a strong foundation in SQL (basic to advanced).
• Practice queries across multiple platforms (Mode, SQLBolt, Kaggle, LeetCode).
• Develop problem-solving skills for data science & AI
• Apply SQL in data pipelines, feature engineering, and MLOps projects.

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📂 Repository Structure

sql_learning_portfolio/
├─ mode_sql_basic/             # Basic SQL queries (Mode Analytics tutorial)
│  ├─ 01_sql_select/
│  ├─ 02_sql_limit/
│  ├─ 03_sql_where/
│  ├─ ...
│  └─ 13_sql_order_by/
│
├─ mode_sql_intermediate/      # Aggregate functions, group by, joins
│  ├─ 01_sql_count/
│  ├─ 02_sql_sum/
│  ├─ 03_sql_minmax/
│  ├─ ...
│  └─ 16_sql_self_joins/
│
├─ mode_sql_advanced/          # Subqueries, data types, window functions
│
├─ kaggle-sql/  # Kaggle SQL notebooks and certificate
│  ├─ intro_to_SQL_certificate.png
│  └─ notebooks/
│     ├─ lesson01_intro_to_sql.ipynb
│     ├─ lesson02_select_from_where.ipynb
│     ├─ ...
│     └─ lesson06_exercise_joining_data.ipynb
│
└─ README.md

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▶️ How to Use This Repo

The queries and notebooks in this repository were created and tested across different platforms:

• mode_sql_basic and mode_sql_intermediate
  All SQL queries in these folders were written and tested using Mode Analytics.
  To run them yourself:

  1. Create a free account on Mode Analytics.
  2. Open the SQL Editor (Mode Studio).
  3. Copy-paste queries from this repo into the editor.
  4. Run them to view results and optionally build charts in the Report Builder.

• kaggle-sql
  Jupyter notebooks in this folder were written and run as part of the Kaggle course:
  👉 Intro to SQL.

  These notebooks run directly on Kaggle using their built-in BigQuery integration and sample datasets.
  If you’d like to try them out:

  1. Open the linked course on Kaggle.
  2. Copy the code into your own Kaggle Notebook, or run the provided .ipynb notebooks from this repo.
  3. Modify queries and re-run cells to explore variations.