📊 Fundamental Data Science

Welcome to my Fundamental Data Science repository! This repo contains coursework, notes, and projects from my Data Science Fundamental course at CADT (Cambodia Academy of Digital Technology) — Year 3, Term 2.

---

📚 Course Overview

This course covers the core concepts and techniques in Data Science, providing a solid foundation for understanding how data can be collected, processed, analyzed, and visualized to extract meaningful insights.

🎯 Learning Objectives

• Understand the Data Science lifecycle and its key stages
• Learn data collection, cleaning, and preprocessing techniques
• Perform Exploratory Data Analysis (EDA) to uncover patterns and trends
• Apply statistical methods and probability concepts to real-world data
• Build and evaluate basic machine learning models
• Create effective data visualizations to communicate findings
• Use Python and its data science ecosystem for hands-on analysis

🛠️ Tools & Technologies

Tool / Library	Purpose
Python	Primary programming language
Jupyter Notebook	Interactive coding environment
NumPy	Numerical computing
Pandas	Data manipulation & analysis
Matplotlib	Data visualization
Seaborn	Statistical data visualization
Scikit-learn	Machine learning

📂 Repository Structure

Fundamental-Data-Science/
├── README.md              # Project documentation
├── notebooks/             # Jupyter notebooks for lessons & exercises
├── datasets/              # Datasets used in the course
├── assignments/           # Course assignments & solutions
└── projects/              # Data science projects

📖 Topics Covered

1. Introduction to Data Science — What is Data Science, roles, and applications
2. Python for Data Science — Python basics, libraries, and data structures
3. Data Collection & Wrangling — Importing, cleaning, and transforming data
4. Exploratory Data Analysis (EDA) — Summary statistics, distributions, and correlations
5. Data Visualization — Creating charts, plots, and dashboards
6. Probability & Statistics — Descriptive & inferential statistics, hypothesis testing
7. Machine Learning Fundamentals — Supervised & unsupervised learning basics
8. Model Evaluation — Accuracy, precision, recall, and cross-validation
9. Real-World Applications — Applying data science to solve practical problems

🚀 Getting Started

Prerequisites

• Python 3.8+
• Jupyter Notebook or JupyterLab

Installation

# Clone the repository
git clone https://github.com/LK-Hour/Fundamental-Data-Science.git
cd Fundamental-Data-Science

# Install required packages
pip install numpy pandas matplotlib seaborn scikit-learn jupyter

Running Notebooks

jupyter notebook

👤 Author

• GitHub: LK-Hour
• Institution: Cambodia Academy of Digital Technology (CADT)
• Program: Year 3, Term 2

📝 License

This repository is for educational purposes as part of the Data Science Fundamental course at CADT.

---

"Data is the new oil. It's valuable, but if unrefined, it cannot really be used." — Clive Humby

Fundamental-Data-Science