CYB 304: Information and Big Data Security



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
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 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.