Big Data Security: Reliability and Protection Strategies
Master big data techniques for enhancing system reliability and security. Learn practical approaches using modern frameworks.
Master big data techniques for enhancing system reliability and security. Learn practical approaches using modern frameworks.
This comprehensive course explores how big data technologies can improve computing system reliability and security. Students learn to apply frameworks like Apache Spark, Flink, and Mesos while addressing security challenges in big data applications. The curriculum covers essential concepts from dependable system design to advanced machine learning security, with hands-on programming projects using real-world datasets.
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What you'll learn
Formulate reliability and security requirements for production systems
Develop big data techniques for enhanced system protection
Implement security measures using modern data processing frameworks
Master machine learning security and adversarial defense strategies
Apply federated learning and differential privacy concepts
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This course provides comprehensive coverage of big data applications in system reliability and security. Starting with foundational concepts in dependable system design, the curriculum progresses through infrastructure security, machine learning robustness, and advanced topics like federated learning and differential privacy. Students gain practical experience through challenging programming projects using real-world datasets, learning to implement security measures using popular big data frameworks.
Big Data Analytics Fundamentals
Module 1 · 8 Hours to complete
Security and Privacy in Big Data
Module 2 · 8 Hours to complete
Machine Learning Models and Validation
Module 3 · 8 Hours to complete
Advanced Learning Techniques and Defense
Module 4 · 8 Hours to complete
Feature Engineering and Federated Learning
Module 5 · 8 Hours to complete
Instructor
Pioneering Leader in Resilient Computing Systems and Cybersecurity Innovation
Dr. Saurabh Bagchi serves as Professor in the School of Electrical and Computer Engineering and Department of Computer Science at Purdue University, where he has established himself as a leading expert in dependable computing and distributed systems since 2002. After completing his B.Tech from IIT Kharagpur and MS/PhD from the University of Illinois at Urbana-Champaign, he began his career at IBM T.J. Watson Research Center before joining Purdue, where he now directs the Center for Resilient Infrastructures, Systems, and Processes (CRISP). His groundbreaking research in distributed systems reliability, cybersecurity, and Internet-of-Things has earned him numerous prestigious accolades including the Alexander von Humboldt Research Award, Adobe Faculty Award, AT&T Labs VURI Award, and Google Faculty Award. As an ACM Distinguished Scientist and member of the IEEE Computer Society Board of Governors, he has published over 850 articles with more than 10,200 citations, while serving as Director of the NSF Center "CHORUS" and co-founding KeyByte LLC as CTO. His impact extends beyond research through his mentorship of 25 PhD and 50 Masters students, who have gone on to successful careers in industry and academia, while his work continues to shape the future of resilient computing systems through innovations in cybersecurity, cloud computing, and defense systems
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