📊 Data Science Learning

This repository is a structured collection of my Data Science learning journey, covering fundamentals, hands-on practice, and projects across multiple tools and technologies.
It is intended for learning, practice, and reference purposes.

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📌 Repository Overview

This repository includes learning materials and practical implementations related to:

• Excel-based data analysis
• Python programming for data science
• SQL for data querying and analysis
• Machine Learning algorithms and models
• End-to-end projects
• Datasets and learning resources used

The content reflects progressive learning, starting from basics and moving toward applied projects.

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📁 Repository Structure

1. Excel Works

• Data cleaning and preprocessing
• Basic to advanced Excel functions
• Pivot tables and analytical summaries
• Exploratory analysis using spreadsheets

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2. Python Learnings

• Core Python concepts
• Data structures and control flow
• Practice programs for logic building
• Python for data analysis basics

Libraries commonly used:

• NumPy
• Pandas
• Matplotlib / Seaborn

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3. SQL Learnings

• Basic SQL queries
• Filtering, sorting, and aggregation
• Joins and subqueries
• Real-world analytical queries
• SQL applied to data analysis use cases

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4. Machine Learning Models

• Supervised learning models
  • Linear Regression
  • Logistic Regression
  • Naive Bayes
  • KNN
  • Decision Trees
  • Support Vector Machines
  • HyperParameter Tuning
• Unsupervised learning basics
• Model evaluation metrics
• Data preprocessing and feature scaling
• Train-test split and cross-validation

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4. Deep Learning

• ANN
• CNN
• RNN

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5. Major Projects

• End-to-end projects including problem statement & datasets used
  • Python Finder (Python)
  • Blogging Website (SQL)
  • Placement Prediction

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6. Datasets

• Publicly available datasets
• Practice datasets for ML and SQL
• Cleaned and raw versions

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7. Resources

• Learning notes
• Reference materials(Textbooks)
• Useful links and documentation

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🎯 Purpose of This Repository

• To document my data science learning journey
• To practice and revise concepts regularly
• To build a strong foundation in analytics and machine learning
• To serve as a reference repository for future projects and interviews

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🛠 Tools & Technologies Used

• Excel
• Python
• SQL
• Jupyter Notebook
• Machine Learning libraries (scikit-learn, pandas, numpy)
• Deep Learning (Tensorflow with Keras)

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🚀 Future Scope

• Add advanced machine learning projects
• Include deep learning implementations
• Integrate real-time data analysis projects
• Improve documentation and explanations

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📬 Contact

For suggestions, improvements, or collaboration, feel free to connect via GitHub.

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