Statistical Decision Making in Data Science with Case Study Understand how Statistics is Applied to Data Science Problem like ANOVA, t-test, F-test in Python Rating: 4.8 out of 54.8 (34 ratings) 16,792 students Description Welcome to the course "Statistical Decision Making in Data Science with a Case Study in Python" You will learn the approaches towards regression with case study. First we start with understanding linear equation and the optimization function value sum of squared errors. With that we find the values of the coefficient and makes least square regression. Then we starts building our linear regression in python. For the model we build we necessary test like hypothesis testing.

User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is scarce or non-existent. We review existing techniques for dealing with this problem and propose a novel alternative based on a principled statistical decision-making view point. We examine the technique on a toy problem and validate it on complex real-world data from an RFID based access control system. The results indicate that it can significantly outperform the classical world model approach. The method could be more generally useful in other decision-making scenarios where there is a lack of adversary data.