Topological Data Analysis (TDA) combines topology and data analytics which offers a new perspective when analyzing data. More so, TDA is capable of providing shape to data that otherwise may be difficult to visualize. In this thesis we provide a brief overview of an algorithm called MAPPER. We analyze two data sets, using statistical techniques and TDA. In the first data set TDA provides a summary where areas with high crime rate are noticeably separate from low crime rates. In the second data set TDA correctly diagnosed benign and malignant tumors in subsets of patients with 100% accuracy. In addition we note a subset of patients that seem to be related, but differ in a given attribute; which changes the model’s diagnosis.