Course Overview
The Applied Data Science for Cybersecurity interactive course will teach cybersecurity professionals how to use data science techniques to quickly manipulate and analyze network & security data and ultimately uncover valuable insights from this data. Through cutting-edge labs and real-world data sets, students will gain real and applicable knowledge in data science specifically applied to cybersecurity challenges.
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 & optimization and finally, implementing at scale. Participants will learn how to read data in a variety of common formats, and then write scripts to analyze and visualize the data in meaningful ways. The course is specifically designed to provide sophisticated training in data science as applied to cybersecurity-related challenges & scenarios. Additionally, it would be beneficial for participants to be comfortable with the basics of Python programming, but it is not required to in order to take this course.
Once you’ve completed this course you have the skills to read data in a variety of common formats then write scripts to analyze and visualize that data.
Course Objectives
Course Syllabus
Day 1: Introduction & Data Engineering
- Introduction to Data Science
- Introduction to Machine Learning
- Overview of Machine Learning & Cyber Applications
- Overview of Data Preparation and Feature Engineering
- Hands-On labs using real-world data sets
Day 2: Data Visualization
- Data Visualization
- Feature Engineering
- Supervised Machine Learning
- Hands-On labs using real-world data sets
Day 3: Machine Learning
- Supervised Machine Learning Cont.
- Model Optimization
- Automated Machine Learning
- Unsupervised Machine Learning
- Hands-On labs using real-world data sets
Day 4: Advanced Topics
- Anomaly Detection
- Introduction to Big Data
- Hunting with Data Science
- Hacking Machine Learning Models
- Overview of Deep Learning
- Hands-On labs using real-world data sets
The Fine Print
Course Size Requirements
- GTK has a 5 student minimum for courses and no maximum head count. Head count must be locked in at the time of contract.
US Travel / Incidental Cost
- U.S. Travel/Incidental cost is $500 per day.
- Virtual courses do not incur travel/incidental costs.
- No Travel/Incidental costs are charged for local courses (MD/DC/VA corridor).
Course Format/Length
- The course is 32 hours in length including labs.
- Instructors typically teach 50% of the day, and run training simulations for the other 50%.
- GTK typically uses Google Meets for virtual courses, but can use any service with advanced notice.
