The analysis of numerical sequential data, such as time series, is a frequent practice in both academic and industrial settings. Offline change detection segments the data retrospectively and is useful for uncovering events and systematic behaviors in data analysis tasks. It is applied in a variety of fields including finance, genomics and energy consumption. Furthermore, in the potential presence of change points, utilizing change detection prior to data modeling can help prevent building inappropriate models under the assumption of data homogeneity, and consequently supports improved prediction and statistical inference. In this thesis, we propose three methods that study the offline change point detection problem from different aspects and application domains. The first method is a nonparametric procedure that can provide computational speedups to simultaneously detect multiple change points. The second method models the relationship between the different channels of multivariate observations to detect change points and anomalies. The third method focuses on the specific biomedical domain of cell culture monitoring to detect the transition from cell growth to confluence. All proposed methods are evaluated through simulations and real-world data applications.