This paper provides a practical guide to density estimation using kernel methods, aiming to encourage practicing statisticians to apply these methods to real data. It covers the basic properties of kernel density estimators, bandwidth selection methods, and recent improvements such as local likelihood density estimates and data sharpening. The paper also includes a real data example from the PGA golf tour to illustrate the methods and compares the performance of different bandwidth selection techniques. Finally, it offers recommendations for density estimation, emphasizing the importance of generating a family of density estimates and using a "center point" bandwidth.This paper provides a practical guide to density estimation using kernel methods, aiming to encourage practicing statisticians to apply these methods to real data. It covers the basic properties of kernel density estimators, bandwidth selection methods, and recent improvements such as local likelihood density estimates and data sharpening. The paper also includes a real data example from the PGA golf tour to illustrate the methods and compares the performance of different bandwidth selection techniques. Finally, it offers recommendations for density estimation, emphasizing the importance of generating a family of density estimates and using a "center point" bandwidth.