General considerations are given about signal processing and its place within data science. It is argued that its specificity is rooted in a balanced implication of tools and concepts from physics, mathematics, and informatics. Examples (Fourier, wavelets) are given for supporting this claim, and arguments are detailed for justifying why time-frequency analysis, which is the topic of this book, can be viewed as a natural language for nonstationary signal processing. The Introduction is also the place where to present a roadmap for the way to read the book.