In today's rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated and elusive. Attackers employ advanced threat hunting techniques to infiltrate systems, often bypassing traditional security measures. For cybersecurity specialists and security professionals, this presents a significant challenge: how can we defend against threats that are designed to evade detection? The answer lies in integrating data science with modern cyber threat hunting practices.

This course is specifically designed for defenders who want to stay ahead of emerging threats by blending human intuition with machine-driven analytics. In the age of data overload, it's not enough to rely on outdated threat detection approaches. Defenders need to harness the power of modern data science tools and techniques to uncover hidden anomalies, detect behavioral patterns, and identify subtle signals of compromise that are the very foundation of effective threat hunting in cyber security. This course equips you with the skills needed to navigate and combat the evolving cybersecurity landscape using cutting-edge cyber threat hunting tools and data science techniques. Throughout the course, you will dive deep into log analysis, threat hunting hypotheses, and machine learning models applied to real-world cybersecurity scenarios. You will gain hands-on experience with industry-standard threat hunting tools like Splunk and Jupyter Notebooks, enabling you to apply learned techniques to live data and active threats within your organization or a structured training environment. Aligned with a practical model for conducting cyber threat hunting, this course is built for defenders who want to sharpen their hunting instincts and use data more effectively. It is ideal for SOC analysts ready to move beyond alert triage, threat hunters seeking to uncover deeper behavioral patterns, blue team engineers building repeatable threat hunting workflows, and cybersecurity students eager to gain hands-on experience with threat hunting Security Information and Event Management (SIEM) platforms like Splunk and Jupyter. Learners should have a basic understanding of Python, familiarity with common log formats, and a solid grasp of core threat hunting cybersecurity concepts. With these foundations in place, you'll move comfortably into data-driven workflows and hands-on cyber threat hunting techniques explored throughout the course. By the end, you'll understand the full threat hunting lifecycle and how machine learning strengthens hypothesis-driven threat investigations. You'll be able to clean, enrich, and visualize raw telemetry; apply threat analysis and anomaly detection techniques like Isolation Forest and DBSCAN; and design a complete ML-powered hunt in Splunk and Jupyter that detects suspicious behavior with clarity and confidence henceforth building the core competencies expected of a skilled cyber threat hunter.