Overview
This 32-hour interactive course teaches cybersecurity professionals to use data science techniques to rapidly manipulate and analyze network and security data and extract actionable insights. The program covers the complete data science lifecycle through hands-on labs using real-world datasets.
50% of class time is instructor-led. The other 50% is hands-on labs and training simulations using Jupyter notebooks in the Centaur VM.
What You Will Learn
- Rapidly explore, visualize, and analyze security data using open source tools
- Analyze large datasets and make data-driven predictions through statistical modeling
- Deploy models to extract meaningful information for decision-making
- Construct, train, evaluate, and deploy supervised machine learning models for security problems
- Build unsupervised models for anomaly detection and exploratory analysis
Course Structure
Day 1: Introduction and Data Engineering Data science and machine learning fundamentals. Machine learning applications in cybersecurity. Data preparation and feature engineering.
Day 2: Data Visualization Visualization techniques for security data. Feature engineering practices. Introduction to supervised machine learning.
Day 3: Machine Learning Advanced supervised learning methods. Model optimization and automated ML. Unsupervised machine learning approaches.
Day 4: Advanced Topics Anomaly detection techniques. Big data introduction. Data science threat hunting. Machine learning adversarial attacks. Deep learning overview.
Topics covered
- Data science and machine learning fundamentals for security
- Data preparation and feature engineering
- Data visualization techniques for security data
- Supervised machine learning for security problems
- Unsupervised machine learning and anomaly detection
- Model optimization and automated ML
- Large language models applied to cybersecurity
- Data science threat hunting
- Machine learning adversarial attacks
- Deep learning overview
- Big data introduction