This specialized course is designed for cybersecurity professionals looking to integrate data science and machine learning into their security operations. With a strong focus on practical application, you'll learn how to use Python, statistical analysis, and machine learning algorithms to detect threats, analyze attack patterns, and automate key aspects of cybersecurity.
Through real-world case studies and hands-on labs, you’ll explore techniques such as anomaly detection, classification, clustering, and predictive modeling—tailored specifically for use in security environments. Tools such as Scikit-learn, Pandas, NumPy, and Jupyter Notebooks are covered in depth, giving you the technical skills to extract actionable insights from complex datasets.
By the end of this course, you'll be able to:
Apply data science methods to identify and respond to cyber threats
Use machine learning models to improve incident detection and response
Build predictive tools for threat intelligence and behavioral analysis
Automate repetitive security tasks through data-driven workflows
Bridge the gap between cybersecurity expertise and data science fluency
Ideal for SOC analysts, incident responders, and security engineers aiming to enhance their analytical capabilities and stay ahead in an AI-driven threat landscape.