Cybersecurity Analytics

In this course students learn to differentiate relational and non-relational databases, and to design a database to an organization's requirements. Preparing raw data for import into a database, using queries to manipulate data, and developing reports to meet stakeholder needs, and database integrity and security are also covered.

Students learn to create and use scripts to access, acquire, prepare, and transform data, including structured and semi-structured data, and to define and detect patterns in unstructured data to demonstrate how real world problems can be answered with data.

In this course students learn about deep learning, machine learning, and artificial intelligence, and compare supervised and unsupervised learning. Machine learning workflows and the challenges associated with machine learning, such as bias and heteroscedasticity, are considered. Students evaluate models, and examine uses for machine learning and artificial intelligence. Natural Language Processing is also addressed.

In Data Visualization in Cybersecurity, students will explore the principles and practices of creating effective visualizations of complex cybersecurity datasets. Throughout the course, students will learn to identify patterns and trends in network traffic, system logs, and other sources of security data using visualization tools and techniques. Additionally, students will learn design principles, reflect on ethical considerations in data visualization, and apply that knowledge through hands-on work and case-studies. By the end of the course, students will have a thorough understanding of how visualizations can support decision-making in cybersecurity analytics.

As cyber threats continue to evolve, organizations need cybersecurity professionals who can leverage data analytics to detect and respond to security incidents quickly and effectively. This course provides students with the skills and knowledge needed to use data analysis and machine learning techniques to identify and mitigate cyber threats. With hands-on experience using industry-standard tools and technologies, students will be prepared to tackle complex security challenges in a wide range of industries. The course's emphasis on real-world scenarios ensures that students are well-equipped to address the practical challenges that arise in today's cybersecurity landscape.

In this course, students will demonstrate the ability to use scripting and database skills to process cybersecurity data for analysis and data analytics techniques to analyze cybersecurity threats. Students will also evaluate cybersecurity strategies and governance policies using data-driven insights, apply machine learning and artificial intelligence techniques to predict cybersecurity threats and vulnerabilities, and use data visualization to communicate complex cybersecurity data effectively. Successful completion of a Capstone Project is required.

In this course students learn about data types and how to perform pre-processing activities including cleaning, integration, reduction, and transformation. Use of appropriate techniques to analyze and model data, such as classification, decision trees, association rules, and clustering, and the causes and methods of detecting outliers are also covered. Students propose additional applications for data mining based on current trends/research.

In this course, students examine essential cybersecurity concepts in the context of different business environments. Students explore a range of topics, including risk, vulnerabilities, security architecture and design, identity and access management, and incident response.

In this course, students learn about threat actors, and threat intelligence collection, analysis, and dissemination. Students also learn how to analyze cyber threats and develop effective countermeasures and examine the legal and ethical implications of threat intelligence gathering and sharing.

In this course, students learn how to develop and implement effective cybersecurity policies and procedures, and study legal and regulatory requirements in cybersecurity, including data protection laws and standards. Students also learn how to align cybersecurity strategies with business objectives.