SQL Practice Journey

A professional collection of my SQL practice, problem-solving exercises, and learning achievements across multiple SQL learning and interview-preparation platforms.

This repository documents my hands-on journey in strengthening SQL skills for Data Analytics, Business Intelligence, and SQL-based technical assessments.

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About This Repository

SQL is one of the core technical skills in my Data Analytics toolkit.

This repository serves as a centralized record of my SQL practice across different platforms, ranging from SQL fundamentals and interactive exercises to analytical and interview-style SQL problems.

The goal is not only to learn SQL syntax, but to develop the ability to:

• Understand data-related problems
• Translate requirements into SQL queries
• Retrieve and filter relevant data
• Aggregate and summarize information
• Work with relational tables
• Apply joins and subqueries
• Solve analytical problems
• Improve logical and query-building skills

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Learning Objectives

Through this practice journey, my objectives are to:

• Strengthen SQL fundamentals
• Develop practical query-writing skills
• Improve analytical problem-solving
• Practice working with relational data
• Become comfortable with SQL joins and subqueries
• Improve query logic and accuracy
• Prepare for SQL-based technical assessments
• Build confidence for Data Analyst interviews
• Apply SQL concepts to analytical scenarios

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SQL Skills Practiced

SQL Fundamentals

• SELECT
• FROM
• WHERE
• DISTINCT
• ORDER BY
• Comparison Operators
• Logical Operators
• LIKE
• IN
• BETWEEN

Aggregation & Analysis

• COUNT()
• SUM()
• AVG()
• MIN()
• MAX()
• GROUP BY
• HAVING

Relational Data

• JOIN
• Multi-table queries
• Subqueries
• Nested queries
• Relational data analysis

Analytical SQL

• Conditional logic
• String operations
• Date and time operations
• Data filtering
• Data aggregation
• Multi-step query logic
• Interview-style problem solving

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Practice Platforms

Platform	Focus
SQL-Practice.com	Interactive SQL query practice
W3Schools	SQL fundamentals and exercises
HackerRank	SQL challenges and problem solving
DataLemur	SQL interview and analytical problems
LeetCode	SQL and database problem solving
SQLZoo	SQL tutorials and query practice

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Repository Structure

sql-practice-journey/
│
├── README.md
│
├── sql-practice.com/
│   ├── README.md
│   └── sql-practice.com.png
│
├── w3schools/
│   ├── README.md
│   └── w3schools.png
│
├── hackerrank/
│   ├── README.md
│   └── hackerrank.png
│
├── datalemur/
│   ├── README.md
│   └── datalemur.png
│
├── leetcode/
│   ├── README.md
│   └── leetcode.png
│
└── sqlzoo/
    ├── README.md
    └── sqlzoo.png

Each platform folder contains:

• A dedicated README.md
• Platform-specific practice information
• A screenshot documenting my practice or completion progress

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Practice Evidence

Screenshots are included within the respective platform folders as supporting evidence of my SQL practice.

Platform	Evidence
SQL-Practice.com	sql-practice.com.png
W3Schools	w3schools.png
HackerRank	hackerrank.png
DataLemur	datalemur.png
LeetCode	leetcode.png
SQLZoo	sqlzoo.png

The screenshots document my practice and progress. They are not intended to claim completion of every exercise available on each platform.

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Relevance to Data Analytics

SQL plays a major role in modern Data Analytics workflows.

This practice journey helps strengthen my ability to work with structured data and supports skills required for:

• Data extraction
• Data filtering
• Data aggregation
• Business reporting
• Exploratory data analysis
• Database querying
• Analytical problem solving
• Technical SQL assessments

These skills complement my broader experience with Python, Pandas, Excel, Power BI, Tableau, and data visualization.

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Learning Approach

My SQL learning approach follows a practical cycle:

Understand the Problem
        ↓
Identify Tables & Columns
        ↓
Determine Required SQL Logic
        ↓
Write the Query
        ↓
Execute & Test
        ↓
Validate the Result
        ↓
Improve the Query

The focus is on problem solving and practical application, rather than memorizing SQL syntax alone.

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Continuous Learning

This repository will continue to evolve as I practice more SQL problems and expand my knowledge of advanced SQL concepts.

Future areas of practice may include:

• Advanced joins
• Complex subqueries
• Common Table Expressions (CTEs)
• Window functions
• Ranking functions
• Conditional aggregation
• Advanced analytical queries
• Query optimization
• Real-world business SQL problems

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Tools & Technologies

• SQL
• Relational Databases
• SQL-Practice.com
• W3Schools
• HackerRank
• DataLemur
• LeetCode
• SQLZoo

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About Me

L Shubham

Aspiring Data Analyst with a focus on SQL, Python, Excel, Power BI, Tableau, and data visualization.

I am building this repository as part of my continuous learning and portfolio development in Data Analytics and Business Intelligence.

Core Data Analytics Skills

• SQL
• Python
• Pandas
• NumPy
• Excel
• Power BI
• Tableau
• Data Visualization
• Exploratory Data Analysis

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Connect With Me

GitHub:

https://github.com/shubham-lingam

LinkedIn:

https://www.linkedin.com/in/shubham-lingam

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Repository Goal

The goal of this repository is to maintain a transparent record of my SQL learning, hands-on practice, and continuous improvement while preparing for opportunities in Data Analytics.

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Author

L Shubham

Focus: Data Analytics | SQL | Business Intelligence | Data Visualization