Data cleaning and preparation are critical steps in the data analysis process, ensuring that data is accurate, consistent, and usable for analysis. Techniques for data cleaning include handling missing data, where missing values can be addressed by either removing the affected rows or columns, or imputing the missing values with mean, median, or mode. Another technique is dealing with outliers, which can skew analysis; outliers can be removed or transformed based on their impact. Additionally, data normalization and standardization are employed to scale data, ensuring consistency across different units of measurement. Duplicate data can be identified and removed to prevent bias in the analysis. Lastly, data type conversion ensures that data is in the correct format, enabling proper interpretation and processing. By applying these techniques, data is made more reliable, paving the way for accurate and insightful analysis.