This course is devoted to the main problems of the sequential analysis: sequential estimation and sequential hypothesis testing. Firstly we construct the least squares estimate for the scalar regression model and then we propose the sequential least squares estimate for the autoregression models. Finally, we study the non-asymptotic properties for the sequential estimation procedures. Then in the second part of this course we construct and study the sequential Wald procedure for hypothesis testing. We study its main properties: the mean times and the optimality properties in the sense of minimal mean time. Then we consider some examples of the Wald procedures. The notes are intended for students of the Mathematical Faculties.