LinkedIn – Protecting Data for Analysis and Machine Learning 2024-5

Protecting Data for Analysis and Machine Learning course. Data security is the process used to maintain the confidentiality, integrity, and availability of an organization’s data. In simpler terms, it means protecting data from unauthorized access, corruption, or theft. Failure to follow the correct principles of data security can have potential consequences for organizations and individuals. In today’s technological world, with the increased use of data in analytics, machine learning models, and artificial intelligence, it is more important than ever for everyone in an organization to have a thorough understanding of data security principles in order to keep the organization safe and secure. Help themselves.

In this course, you will learn the basics of data security and the potential consequences of ignoring it. Instructor Monica Royal explores best practices for securing your data analytics pipeline and demonstrates some of the most common data anonymization techniques. This course is suitable for beginners in the field of data security. There is no prerequisite to take this course.

What you will learn:

• Understanding the concept of data security
• Types of data to be protected
• Potential consequences of poor data security
• Data life cycle and how to protect data at each stage
• Best training and education practices for data security
• Data anonymization techniques, such as masking, encryption, generalization, scrambling, and aliasing

This course is suitable for people who:

• Are interested in learning the principles of data security
• They work in the field of data analysis, machine learning or artificial intelligence
• They want to protect their data from unauthorized access, corruption or theft
• Looking to improve their skills in the field of cyber security