knowledge and insights from data. Here are some key components and concepts:
1. **Data Collection**
- **Types of Data**: Structured, unstructured, and semi-structured.
- **Data Sources**: Databases, APIs, web scraping, sensors, and user-generated content.
2. **Data Preparation**
- **Cleaning**: Handling missing values, removing duplicates, and correcting errors.
- **Transformation**: Normalization, scaling, and encoding categorical variables.
- **Integration**: Combining data from different sources.
3. **Exploratory Data Analysis (EDA)**
- **Descriptive Statistics**: Mean, median, mode, variance, and standard deviation.
- **Visualization**: Histograms, box plots, scatter plots, and correlation matrices.
- **Patterns and Anomalies**: Identifying trends, outliers, and correlations.
4. **Statistical Foundations**
- **Probability Theory**: Basics of probability, random variables, and distributions.
- **Hypothesis Testing**: Null and alternative hypotheses, p-values, and significance levels.
1. **Data Collection**
- **Types of Data**: Structured, unstructured, and semi-structured.
- **Data Sources**: Databases, APIs, web scraping, sensors, and user-generated content.
2. **Data Preparation**
- **Cleaning**: Handling missing values, removing duplicates, and correcting errors.
- **Transformation**: Normalization, scaling, and encoding categorical variables.
- **Integration**: Combining data from different sources.
3. **Exploratory Data Analysis (EDA)**
- **Descriptive Statistics**: Mean, median, mode, variance, and standard deviation.
- **Visualization**: Histograms, box plots, scatter plots, and correlation matrices.
- **Patterns and Anomalies**: Identifying trends, outliers, and correlations.
4. **Statistical Foundations**
- **Probability Theory**: Basics of probability, random variables, and distributions.
- **Hypothesis Testing**: Null and alternative hypotheses, p-values, and significance levels.
