Morning SessionThis 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?
- How can I tell timeseries and graph data apart?
- What makes “deep” learning different from other approaches?
- How can I effectively work with others in my organization to achieve data science success?
Afternoon SessionThe course continues by providing a hands-on 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. For both practitioners and managers, applying data science to cybersecurity applications can be a challenge. 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 tools do I need to get started with data science?
- Where can I get data for exploring particular algorithms?
- I managed to choose an algorithm; now how do I make it work?
- What does a working data science model look like?
- I (finally) got a model, how do I know if it performs well?
Intended Audience: Practitioners, managers, and/or executives who are curious about data science and want to strengthen their understanding of data science concepts and techniques in a hands-on, introductory setting. Experience with applied math, statistics, and/or coding is beneficial, but not required.
