## 2. Key Components of Data Science
### a. Statistics and Probability
- Foundation for analyzing data and building predictive models.
- **Key concepts:** descriptive statistics, hypothesis testing, and probability distributions.
### b. Machine Learning and Artificial Intelligence
- Enables systems to learn from data and make predictions.
- **Example:** Predicting stock prices or recommending products.
### c. Data Wrangling and Preprocessing
- Cleaning and transforming raw data into a usable format.
- Includes handling missing values, removing duplicates, and feature engineering.
### d. Data Visualization
- Communicating findings using graphs, charts, and dashboards.
- **Tools:** Tableau, Matplotlib, and Power BI help represent insights effectively.
