# Data Science and Machine Learning Basics

## What is Data Science?

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

## The Data Science Process

1. **Data Collection**: Gathering data from various sources
2. **Data Cleaning**: Handling missing values, outliers, and inconsistencies
3. **Exploratory Data Analysis (EDA)**: Understanding patterns, relationships, and trends
4. **Feature Engineering**: Creating new features from existing ones
5. **Modeling**: Building predictive models using machine learning algorithms
6. **Evaluation**: Assessing model performance
7. **Deployment**: Implementing the model in a production environment

## Common Machine Learning Algorithms

### Supervised Learning
- **Linear Regression**: Predicts continuous values
- **Logistic Regression**: Used for binary classification
- **Decision Trees**: Tree-like model of decisions
- **Random Forest**: Ensemble of decision trees
- **Support Vector Machines (SVM)**: Classifies data by finding the hyperplane

### Unsupervised Learning
- **K-means Clustering**: Groups similar data points together
- **Hierarchical Clustering**: Creates a tree of clusters
- **Principal Component Analysis (PCA)**: Reduces dimensionality

## Tools and Libraries

- **Python**: The most popular language for data science
- **Libraries**: NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch
- **Jupyter Notebooks**: Interactive computing environment
- **R**: Statistical programming language
- **SQL**: For database querying and manipulation