This book offers an introduction to key topics in spectral graph theory. In spectral graph theory, various properties of graphs are studied using methods from linear algebra, particularly through the eigenvalues and eigenvectors of different matrices that describe the graph structure. Various aspects of graph theory find applications within the field of data science. In this book, the necessary foundations of abstract graph theory and linear algebra are covered in parallel, making it suitable for students in their early semesters. The book has been tested multiple times in one-semester-long lectures and is therefore well-suited as a basis for a course and a collection of exercises for instructors.