CYB 304: INFORMATION AND BIG DATA SECURITY

Information and Big Data Security   (2 Units C: LH 15; PH 45) 

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 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 utilizing 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 organizational 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 building corporate Big Data and data science platforms. Hands-on exposure to Hadoop and Spark (or any of the BD tools), build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases. Practical assignment of information and BD security.