Data transformations are mathematical operations that alter the scale or shape of your data, enabling you to achieve various goals in your exploratory data analysis (EDA). Common transformations include logarithmic, square root, power, standardization, and min-max normalization. Logarithmic transformation reduces right skewness and variance by applying the natural logarithm function and is useful for variables with a multiplicative relationship, such as income or population. Square root transformation reduces right skewness and variance by applying the square root function and is suitable for variables with a positive lower bound, such as counts or areas. Power transformation reduces skewness and variance by applying a power function with a parameter lambda and is ideal for variables with a range of values, such as heights or weights. Standardization rescales data to have a mean of zero and a standard deviation of one by subtracting the mean and dividing by the standard deviation and is useful for variables that have different scales or units, such as age or income. Lastly, min-max normalization rescales data to have a minimum of zero and a maximum of one by subtracting the minimum and dividing by the range. It is appropriate for variables with a fixed range, such as percentages or ratings. To apply these transformations, you can use various tools and libraries such as pandas, numpy, scipy, or scikit-learn in Python or dplyr, tidyr, or caret in R.