Course Objectives:

The objectives of this course are to:

  1. Provide a comprehensive understanding of information and big data security concepts, tools, and methodologies.
  2. Explore the unique challenges, policies, and techniques for ensuring security in big data systems.
  3. Equip students with practical skills in big data handling, cloud-based data analysis, and machine learning applications.
  4. Develop the ability to analyze and implement big data solutions to address organizational challenges.
  5. Bridge the theoretical understanding of big data security with practical, hands-on experience using industry-standard tools.
  6. Introduce students to governance, adoption strategies, and lifecycle management of information and big data security.

 

Overall Learning Outcomes:

At the end of this course, students should be able to:

  1. Describe the key concepts of information security, big data, its characteristics, and tools/technologies for handling it.
  2. Explore challenges and requirements of big data security and lifecycle security management.
  3. Identify and implement policies and methodologies for securing information and big data systems.
  4. Apply knowledge of risk management, systems engineering, and big data handling techniques.
  5. Examine big data platforms, adoption strategies, components, and governance, including cloud-based solutions.
  6. Analyze how big data drives organizational changes and the analytical tools/techniques used in solution development.
  7. Apply machine learning techniques, analyze big data recommendations, and utilize cloud-based systems for data analysis.
  8. Design enterprise-scale, cost-efficient big data and machine learning solutions.

 

Detailed Course Content:

Module 1: Introduction to Big Data

  • Definitions: Small Data vs. Big Data
  • Evolution and characteristics of Big Data (3Vs/6Vs)
  • Importance, sources, formats, and applications of Big Data
  • Business intelligence vs. Big Data vs. Data mining

Module 2: Big Data Challenges and Technologies

  • Unique features and challenges of Big Data
  • Handling techniques and technologies for Big Data
  • Cloud computing and its role in Big Data management
  • Operational vs. Analytical Big Data

Module 3: Big Data Solutions and Security

  • Big Data handling techniques: Data acquisition and processing
  • Security requirements for information and Big Data systems
  • Lifecycle security management of information systems
  • Governance, risk management, and policies for Big Data security

Module 4: Practical Applications of Big Data

  • Big Data platforms, components, and adoption strategies
  • Developing Big Data solutions: Tools and techniques
  • Organizational changes driven by Big Data analysis
  • Case studies: Preparing reports and presentations

Module 5: Machine Learning and Cloud-based Big Data Analysis

  • Applying machine learning techniques to Big Data analysis
  • Using programming languages like R for analysis and dashboards
  • Cloud-based solutions for Big Data analytics
  • Practical exposure to Hadoop, Spark, and NoSQL databases

Module 6: Legal and Ethical Aspects of Big Data

  • Laws related to information security and data management
  • Privacy issues and ethical concerns in Big Data analytics

 

Teaching/Learning Methods:

  • Lectures with visual aids and case study discussions.
  • Hands-on lab sessions using tools like Hadoop, Spark, and R.
  • Group assignments and problem-solving exercises.
  • Practical demonstrations of Big Data security and governance concepts.

 

Modes of Assessment:

  1. Assignments and Class Participation: 20%
    • Written assignments and active participation in discussions.
  2. Lab Practicals: 40%
    • Hands-on data analysis, machine learning dashboards, and security tasks.
  3. Mid-Semester Test: 20%
    • Test on theoretical aspects of Big Data security and methodologies.
  4. Final Examination/Project: 20%
    • Comprehensive evaluation of skills in Big Data solution development.

 

Reading List/References:

Primary Texts:

  1. Zikopoulos, P. C., Eaton, C., deRoos, D., Deutsch, T., & Lapis, G., Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw-Hill Education, 2016.
  2. Furht, B., & Villanustre, F., Big Data Technologies and Applications, Springer, 2020.

Supplementary Texts:

  1. Chen, M., Mao, S., & Liu, Y., Big Data: Related Technologies, Challenges, and Future Prospects, Springer, 2018.
  2. Agneeswaran, V. S., Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives, Pearson, 2017.

Additional Resources:

  1. Hadoop and Spark Official Documentation: https://hadoop.apache.org/ and https://spark.apache.org/
  2. Tutorials and online resources on R programming and Big Data analysis.
  3. Research articles on Big Data security and governance trends.