Applied Data Science and Machine Learning for Cybersecurity

This course will be offered over four (4) weeks. Each week will have a total of eight hours of coursework spread out over two disparate days. The course will be taught from 12:00 p.m. until 4:00 p.m. each day. (1200-1600) The course dates are:

• October 12, 14, 19, 21, 26 & 28
• November 2 & 4

This interactive course will teach security professionals how to use data science techniques to quickly manipulate and analyze network and security data and ultimately uncover valuable insights from this data. The course will cover the entire data science process from data preparation, feature engineering and selection, exploratory data analysis, data visualization, machine learning, model evaluation and optimization and finally, implementing at scale—all with a focus on security-related problems.

By the end of the course, students will be able to:

• Use the python data science ecosystem to rapidly prepare, explore and visualize cybersecurity data
• Build and evaluate common machine learning models and apply these techniques to cybersecurity use cases
• Develop unsupervised models to uncover anomalies and other patterns in their cybersecurity data.

Anyone who wishes to incorporate automated data analysis, machine learning and data science into their cybersecurity work. Particularly those working in the following job roles:

• Security Analyst
• SOC Analysts
• SOC Engineers
• CND Analysts
• Security Monitoring
• System Administrators
• Cyber Threat Investigators
• Individuals working on a network hunt team

Intermediate/Advanced

This is a hands-on course. To get the most out of the class and labs, students should be comfortable coding in Python as well as understand common security and network concepts.