---

✨ "Transforming raw data into elegant, meaningful insights"

---

📑 Table of Contents

• 🎯 About This Repository
• � Learning Workflow
• 📂 Repository Structure
• 📚 Assignment Details
• 💾 Datasets Overview
• 🚀 Quick Start Guide
• ✦ Tech Stack & Arsenal
• 📈 Learning Progress
• 🌟 Key Takeaways

---

🎯 About This Repository

Welcome to my Data Science Learning Hub! This repository is a comprehensive collection of assignments, projects, and hands-on exercises completed during my internship journey. Each notebook represents a milestone in mastering the essential tools and techniques of data science.

🔍 What You'll Find Here

Category	Topics Covered
🐍 Python Fundamentals	Variables, Control Flow, Functions, OOP
📦 Data Structures	Lists, Dictionaries, Tuples, Sets
🔢 NumPy	Arrays, Broadcasting, Linear Algebra
🐼 Pandas	DataFrames, Data Cleaning, Transformation
📊 EDA	Visualization, Pattern Recognition, Insights
📉 Statistics	Hypothesis Testing, Probability, Distributions

---

🔄 Learning Workflow

Below is the structured learning path and workflow of my data science journey across the assignments:

---

📂 Repository Structure

📁 ASSIGNMENT/
├── 🐍 Python Basics Assignment Day 5.ipynb
├── 🗃️ Python Data Structure Assignment Day 5.ipynb
├── 🔢 Numpy_Assignment_Day_10.ipynb
├── 🐼 Pandas Assignment Day 10.ipynb
├── 📁 CARS DATASET/
│   ├── 📊 EDA_Assignment_Day_14.ipynb
│   └── 📄 Cars_data.csv
└── 📁 statistics Module/
    ├── 📈 Statistics Module Assignment.ipynb
    └── 📄 Telecom Dataset.csv

---

📚 Assignment Details

🐍 Python & Data Structures

📘 Python Basics (Day 5) 📓 Open Notebook Concept Description Variables Data types & type casting Control Flow if-else, loops Functions Definition & parameters Error Handling try-except blocks

📗 Data Structures (Day 5) 📓 Open Notebook Structure Operations Lists Indexing, slicing, methods Dictionaries Key-value pairs, iteration Tuples Immutable sequences Sets Unique elements, set ops

---

📊 Data Science Libraries

🔢 NumPy Mastery (Day 10) 📓 Open Notebook # Skills Demonstrated ✅ Array creation & manipulation ✅ Broadcasting & vectorization ✅ Mathematical operations ✅ Statistical functions ✅ Array indexing & slicing

🐼 Pandas Proficiency (Day 10) 📓 Open Notebook # Skills Demonstrated ✅ DataFrame operations ✅ Data cleaning & preprocessing ✅ Groupby & aggregations ✅ Merging & joining ✅ Handling missing values

---

📈 Exploratory Data Analysis

🚗 Cars Dataset EDA (Day 14)

📓 Open Notebook | 📄 Dataset

Analysis Type	Description
🔍 Data Profiling	Shape, dtypes, missing values
📊 Univariate Analysis	Distribution of individual features
📈 Bivariate Analysis	Relationships between variables
🎨 Visualizations	Histograms, box plots, scatter plots
💡 Insights	Key findings & recommendations

---

📉 Statistical Analysis

📊 Statistics Module Assignment

📓 Open Notebook | 📄 Dataset

📌 Topics Covered:
├── Descriptive Statistics (Mean, Median, Mode, Std Dev)
├── Probability Distributions (Normal, Binomial, Poisson)
├── Hypothesis Testing (t-test, chi-square, ANOVA)
├── Correlation & Regression Analysis
└── Confidence Intervals

---

💾 Datasets Overview

Dataset	Description	Size	Format
🚗 Cars Dataset	Automobile specifications & prices	-	CSV
📞 Telecom Dataset	Customer data for statistical analysis	-	CSV

---

🚀 Quick Start Guide

Prerequisites

# Required packages
pip install numpy pandas matplotlib seaborn jupyter scipy

Installation

# 1️⃣ Clone the repository
git clone https://github.com/Piyu242005/Piyu-ASSIGNMENTS.git

# 2️⃣ Navigate to the project directory
cd Piyu-ASSIGNMENTS

# 3️⃣ Install dependencies (optional: use virtual environment)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

# 4️⃣ Launch Jupyter Notebook
jupyter notebook

---

✦ Tech Stack & Arsenal

Core Technology	Specialty
	Algorithm & Core Logic
	High-Performance Computation
	Advanced Data Manipulation
	Interactive Storytelling
	Statistical Modeling

---

📈 Learning Progress

Tracking my journey through the data science curriculum:

Module	Timeline	Progress	Status
Python Basics & Data Structures	Day 01-05		✅ Mastered
NumPy & Pandas	Day 06-10		✅ Mastered
Exploratory Data Analysis	Day 11-14		✅ Mastered
Statistics Module	Day 15-XX		🔄 Ongoing

---

🌟 Key Takeaways

• 💡 Python Mastery: Strong foundation in Python programming
• 📊 Data Wrangling: Proficient in data manipulation with Pandas
• 🔢 Numerical Computing: Comfortable with NumPy operations
• 📈 Data Visualization: Creating meaningful visual insights
• 📉 Statistical Analysis: Understanding data through statistics

---

🤝 Connect With Me

---

⭐ If you find this repository helpful, please consider giving it a star!

Made with ❤️ by Piyu