Information And Big Data Security
Introduction to big data. Small data vs. big data. What is big data? The evolution of data/big data. Big data characteristics-3Vs/6Vs. Unique features of big data. Importance of big data? Why does big data matter? Sources of big data. Formats of data. Applications of big data. Use case- issues and…
Learning outcomes
At the end of this course, students should be able to: 1. describe information security, big data, big data characteristics, techniques, tools and technologies, operational and analytical big data; 2. explore information and big data security, challenges, requirements, and the lifecycle security management; 3. identify the basic policies on information and big data security methodologies; 4. apply knowledge of information and big data security risk management, security; 5. policies, security in the systems-engineering process and big data handling techniques; 6. examine big data skills, adoption, platform, components and governance, and how to use the cloud for big data; 7. analyse how big data is driving organisational change, and essential analytical tools and techniques used in developing big data solutions; and 8. apply machine learning techniques, analyse big data recommendations, and cloud-based big data analysis.
Course contents
Introduction to big data. Small data vs. big data. What is big data? The evolution of data/big data. Big data characteristics-3Vs/6Vs. Unique features of big data. Importance of big data? Why does big data matter? Sources of big data. Formats of data. Applications of big data. Use case- issues and solutions. Big data technology. Big data as an opportunity. Example of big New Computing 75 data. Big data statistics. Business intelligence vs. big data vs. data mining. Big data handling and techniques. Using the cloud for big data. Big data challenges/problems. How businesses are utilising big data. Big data technologies. Operational and analytical big data. Big data skills. Big data adoption. Big data analysis in practice. Case study session, preparation of case study report and presentation. The big data platform and key aspects. Governance for big data. Big data components. Big data driven organisational change and essential analytical tools and techniques. Develop big data solutions. System and management view of information and big data security. Requirements for information and big data security. Systems-design process and lifecycle security management of information systems. Basic policies on information security and methodologies. Information-security risk management, security policies, security in the systems-engineering process. Laws related to information security and management of operational systems. Apply machine learning techniques and other big data programming languages. Analyse big data recommendations. Cloud-based big data analysis. Lab work: Practice on data acquisition and how to initiate discovery on raw data using discovery systems. Learn Big Data analytics skills. Practical procedure for the crafting of an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from data. Use the practical exercises to bridge the gap between the theoretical world of technology with the practical ground reality of…