FloCon 2020

This course provides an accessible introduction to foundational data science concepts, terminology, and approaches using cybersecurity examples and use cases.  Data science is rapidly becoming an integral part of the network security industry. Although widespread applications of data science in network security are relatively recent, data science has roots going back decades. Due to its depth and technical complexity, Data Science is often considered to be indistinguishable from magic.  This course is intended to break the illusion and help attendees harness the true power of data science to defend networked systems.
The morning session will answer important questions, including:

• Are data science and machine learning truly different from artificial intelligence?
• Is this product really using machine learning or just faking it?
• I have a data science model, now what do I do?
• How can I tell timeseries and graph data apart?
• What makes “deep” learning different from other approaches?

Intended Audience: Practitioners, managers, and/or executives who are curious to strengthen their understanding of data science concepts and techniques in an accessible, introductory setting. There are no prerequisites for this course; however, an understanding of math, statistics, and coding is helpful.

AI has infused the network security landscape, fundamentally changing how network security is practiced. This course is intended to help managers and decision makers understand this new reality. We will cover a number of topics relevant to organizational leaders, including:

• How AI provides value to the field of network security
• Differentiating between AI-enabled capability and traditional security tools (many of which still work great!)
• Understanding fundamental AI limitations
• How to effectively build, manage, and succeed with an integrated data science / network security team

The morning session will focus on understanding AI capability.

Intended Audience: This course is targeted for cyber analysts who are interested in transitioning from analysis to data science at a level that should not require any statistical or machine learning background. The goal is to introduce foundational data science concepts and prepare attendees to scope a new AI/ML project. Managers or individuals planning to fund such a project will gain practical insight into the difference in organizing a data science project.

This course provides an accessible introduction to foundational data science techniques and algorithms using cybersecurity examples and use cases.  Data science is rapidly becoming an integral part of the network security industry. Although widespread applications of data science in network security are relatively recent, data science has roots going back decades. Due to its depth and technical complexity, data science is often considered to be indistinguishable from magic.  This course is intended to demystify data science and show how specific data science techniques can be applied to network data.
The afternoon session answers important questions including:

• What is data science, anyways? (short version)
• I don’t have enough data.  What do I do? Or worse, I have too much data!  What do I do?
• Too many algorithms, which one do I choose (if any)?
• I managed to choose an algorithm; now how do I make it work?
• I (finally) got a model, did I do it right?

Intended Audience: Practitioners, managers, and/or executives who are curious to strengthen their understanding of data science concepts and techniques in an accessible, introductory setting. Experience with applied math, statistics, and/or coding is beneficial, but not required.

AI has infused the network security landscape, fundamentally changing how network security is practiced. This course is intended to help managers and decision makers understand this new reality. We will cover a number of topics relevant to organizational leaders, including:

• How AI provides value to the field of network security
• Differentiating between AI-enabled capability and traditional security tools (many of which still work great!)
• Understanding fundamental AI limitations
• How to effectively build, manage, and succeed with an integrated data science / network security team

The afternoon session will dive into team-building and management.

Intended Audience: This course is targeted for cyber analysts who are interested in transitioning from analysis to data science at a level that should not require any statistical or machine learning background. The goal is to introduce foundational data science concepts and prepare attendees to scope a new AI/ML project. Managers or individuals planning to fund such a project will gain practical insight into the difference in organizing a data science project.