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.

Learning objectives
Apply data anonymization techniques, including masking, encryption, generalization, perturbation, and pseudonymization, to protect sensitive information in datasets.
Evaluate the different storage options for data and implement best practices to secure stored data, including encryption and regular backups.
Utilize pre-processing techniques such as data anonymization to minimize privacy risks in data analysis and machine learning modeling.
Identify key personal information (PII) within datasets and assess the implications of using such data in analysis and model training.
Execute data disposal strategies in accordance with a company’s data retention policy to reduce the risk of data leakage or theft.
Organize and share data securely, employing encrypted channels and access controls to prevent unauthorized data access.
Design and participate in data privacy and security awareness training, emphasizing the importance of regular and engaging education for all employees.
Build a foundational understanding of data privacy regulations and how they influence data collection, storage, analysis, and disposal practices.