This book gives a systematic, comprehensive & unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals & time series analysis. The coverage is suitable for a one-semester course for advanced undergraduate & graduate students with majors ranging from statistics & engineering to medicine, business & the social sciences. The prerequisites are intermediate calculus & introductory probability.The companion software package, available over the World Wide Web, brings all the discussed topics into the realm of reproducible research. Virtually every claim & development mentioned in the book are illustrated with graphs which are available for the reader to reproduce & to modify, making the material fully transparent & allow one to study it interactively. This feature, together with the intuitive & informal style of presentation, makes it extremely readable & appealing to novices, ranging from graduate students to mature researchers venturing for the first time into this area.The main emphasis of the book is on small sample properties of the proposed data-driven orthogonal series (both Fourier & wavelets) estimators & on graphical presentation of the results. This makes the book of a special interest to practitioners.