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I am a security professional with a few years under my belt and I’ve realized I would be more effective if I were able to parse and interpret data more effectively and efficiently. I know there is probably something to this whole machine learning thing and I want to understand what that is and how I might be able to apply it in my role.
Pre-training content options:
Python for Data Analysis: Wes McKinney
Post-training: 'Further Learning' content options:
For Python users:
We will be using the Griffon Virtual Machine which is available here: https://github.com/gtkcyber/griffon-vm. In order to run Griffon, you will need:
While we strongly encourage you to use Griffon, if you choose not to, you will need:
Join experts Jay Jacobs and Charles Givre for a hands-on, in-depth exploration of data analysis and machine learning in cybersecurity. In this course, you’ll learn how to explore and analyze data you probably already have and gain valuable exposure to and experience with tools and techniques to prepare, analyze, and visualize the knowledge hiding in your data. Jay and Charles guide you through working with three hands-on, practical applications with real data, introducing each in a language-agnostic approach before providing language-specific guidance for hands-on work. A GitHub repository with the examples will be available so that you can revisit the examples and continue learning after the training.
Charles Givre is an unapologetic data geek who is passionate about helping others learn about data science and become passionate about it themselves. For the last five years, Charles has worked as a data scientist at Booz Allen Hamilton for various government clients and has done some really neat data science work along the way, hopefully saving US taxpayers some money. Most of his work has been in developing meaningful metrics to assess how well the workforce is performing. For the last two years, Charles has been part of the management team for one of Booze Allen Hamilton’s largest analytic contracts, where he was tasked with increasing the amount of data science on the contract—both in terms of tasks and people.
Even more than the data science work, Charles loves learning about and teaching new technologies and techniques. He has been instrumental in bringing Python scripting to both his government clients and the analytic workforce and has developed a 40-hour Introduction to Analytic Scripting class for that purpose. Additionally, Charles has developed a 60-hour Fundamentals of Data Science class, which he has taught to Booz Allen staff, government civilians, and US military personnel around the world. Charles has a master’s degree from Brandeis University, two bachelor’s degrees from the University of Arizona, and various IT security certifications. In his nonexistent spare time, he plays trombone, spends time with his family, and works on restoring British sports cars.
Jay Jacobs is the senior data scientist at BitSight Technologies. Prior to joining BitSight, Jay spent four years as the lead data analyst for the Verizon Data Breach Investigations Report. Jay is the coauthor of Data-Driven Security, which covers data analysis and visualizations for information security, and hosts the Data-Driven Security and R World News podcast. Jay is also a cofounder of the Society of Information Risk Analysts and currently serves on its board of directors. Jay is also active in the R community; he coordinates his local R user group for the greater Minneapolis area and contributes to local events and functions supporting data analysis.
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