Learn how data science can help improve network security by analyzing the recent development in the network security market and its movement towards incorporating machine learning and big data analytics technologies. Jungwoo points out that one of the frontiers of network security is the area of logging and monitoring and their adoption of data science technologies. He also introduces newly emerging network security services taking advantage of this new trend.
- [Voiceover] As cyber threats increase, more organizations are making network security as their top priority. The attacks on the internet are getting more sophisticated at light speed while network security vendors are always trying to catch up with the advances made in new hacking techniques. Despite many recent stories of network vulnerabilities being breached, it is an encouraging sign that many of the network security solutions providers are now moving towards leveraging machine learning and big data analytics to enhance their products.
One of the frontiers of network security is the field of logging and monitoring. Many of the software companies offering network security solutions are incorporating machine learning and big data analytics into their product line. Microsoft is a great example of this trend. Through their cloud product called Azure, they offer a machine learning service on which users can build their own intrusion detection solutions or use any of the built-in services provided by Microsoft.
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and analytics, explores which markets are using big data the most, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the four leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why analytics is important
- How data science is used in social media, climate research, and more
- Data science skills
- Data science certifications
- The future of big data