SQL + Python Practice Projects

📌 Overview

This repository documents my SQL and Python learning journey, combining hands-on practice projects, real-world case studies, and statistics-based analysis.
All projects are built step-by-step with the help of Generative AI (ChatGPT / Codey), focusing on understanding and logic building rather than copy-pasting code.

The repository contains a mix of practice exercises, database projects, and analytical case studies, reflecting continuous learning and applied problem-solving using SQL, Python, and data analysis concepts.

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📂 Repository Structure

🔹 SQL + Python Practice Projects

• cars_owners_project/ – Vehicle and owner tracking system
• d1_studentproject/ – Basic student management (Python + SQL)
• d2_stuproject/ – Extended student project with queries
• d3_student_marks_status/ – Student marks tracking system
• d4_electronic_shop/ – Shop management (inventory + sales)
• d5_library_book_management_system/ – Library management (borrowing & returning)
• d6_school_management/ – School management database with Python interface
• d7_stud_course_enrollment_project/ – Student course enrollment (SQL + Python integration)
• d8_company_project_tracker/ – Company project tracking system

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🔹 SQL Practice & Databases

• sql_queries_cgpt/ – SQL query practice tasks
• sqlite/ – SQLite database files for testing
• studentmarks_usingsql/ – SQL-based student marks analysis
• Chinook Database SQL Practice – joins, subqueries, aggregations, grouping, and analysis on a real-world sample database

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🔹 Data Analysis & Case Studies

📊 Diwali Sales Analysis

• Exploratory Data Analysis (EDA) using Python and Pandas
• Customer demographics, product categories, and sales trends
• Visualizations using Matplotlib and Seaborn
• Business-focused insights and conclusions

🧠 HR Analytics Case Study – Employee Performance & Retention

• Employee performance and retention analysis
• Data cleaning and exploratory analysis
• Identification of key factors affecting performance and attrition
• Data-driven insights and recommendations

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🔹 Statistics (Internshala Certification – Practice & Notes)

• Descriptive Statistics (mean, median, mode, variance, standard deviation)
• Data distribution and interpretation
• Hypothesis Testing concepts
• Null and Alternative Hypothesis
• Type I and Type II Errors
• Z-test, T-test, Chi-square test
• Correlation analysis
• Basics of Linear Regression

Practice notebooks focus on understanding statistical concepts through data, not just formulas.

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🛠️ Skills Practiced

• SQL – SELECT, WHERE, JOIN, GROUP BY, HAVING, DISTINCT, subqueries, aggregation
• Python – Pandas, NumPy, database connections, CRUD operations, logic building
• Data Analysis & EDA – cleaning, grouping, visualization, insight writing
• Statistics – descriptive stats, hypothesis testing, correlation, regression
• Problem Solving – translating real-world scenarios into SQL + Python solutions
• Hands-on Practice – writing code manually to strengthen fundamentals

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✨ Key Takeaways

• Strengthened SQL query writing and debugging skills
• Gained confidence in connecting SQL with Python
• Practiced real-world scenarios like sales analysis, HR analytics, and management systems
• Improved ability to derive insights from data and document them clearly
• Built consistency and confidence by writing code by hand and iterating with AI guidance

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⚠️ Notes

• Some datasets contain encoded categorical values without explicit documentation; insights are based strictly on observed data patterns.
• This repository is actively updated as I continue learning and building new projects.

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👉 This repository reflects my continuous learning journey with SQL, Python, and Statistics, using AI as a coding partner and focusing on practice, understanding, and project-based learning.