Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of statistics, computer science, and domain expertise to analyze and interpret complex data.

Key components of data science include:

1. **Data Collection**: Gathering data from various sources, such as databases, web scraping, or sensors.
2. **Data Cleaning**: Preparing the data by handling missing values, removing duplicates, and correcting inconsistencies.
3. **Exploratory Data Analysis (EDA)**: Using statistical and graphical techniques to understand the data's structure and patterns.
4. **Feature Engineering**: Creating new variables or modifying existing ones to improve the performance of models.
5. **Model Building**: Applying statistical and machine learning algorithms to make predictions or discover patterns.
6. **Evaluation**: Assessing the performance of models using metrics like accuracy, precision, and recall.
7. **Deployment**: Integrating models into applications or systems for real-world use.
8. **Communication**: Presenting findings and insights to stakeholders through visualizations and reports.

Data scientists need skills in programming (e.g., Python, R), statistical analysis, and data manipulation, along with domain knowledge to effectively interpret and leverage data.