Protecting Data for Analysis and Machine Learning

Data security is the process of maintaining the confidentiality, integrity, and availability of an organization’s data. More simply put, it is the process of protecting data from unauthorized access, corruption, or theft. There are several potential consequences that organizations and individuals can face due to bad data security practices. In today’s tech landscape with the increase in use of data in analysis, machine learning models, and AI, it is more important than ever for everyone in an organization to have a solid understanding of data security practices in order to help keep organizations–and themselves–safe. In this course, learn the basics of data security and its potential consequences if ignored. Instructor Monica Royal explores the best practices for protecting the data analytics pipeline and demonstrates some of the most common data anonymization techniques.