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Cybersecurity Management · Proposed course

Cyber Data Analytics & Visualization

Course code
CSM 602
Credit hours
3
Program
MSCM
Delivery
Hybrid/Online

Course description

Course overview

This course focuses on the application of data analytics and visualization techniques within the field of cybersecurity. Students will learn how to collect, analyze, and interpret cybersecurity data using statistical tools and programming languages. Emphasis is placed on identifying threats, visualizing attack patterns, and leveraging analytics for predictive security modeling and decision-making.

Course content is presented for institutional review and remains subject to approval and revision.

Academic focus

Course objectives

  1. 01

    Understand data analytics concepts and their applications in cybersecurity contexts.

  2. 02

    Utilize Python, R, or specialized cybersecurity analytics tools for data analysis and visualization.

  3. 03

    Apply statistical and machine learning models to detect and predict cybersecurity incidents.

  4. 04

    Develop effective dashboards and reports for cyber threat monitoring and response.

Learning outcomes

Upon completion

  1. CLO 1

    Apply quantitative data analysis techniques to cybersecurity datasets (maps to PLO 2, PLO 3).

  2. CLO 2

    Utilize visualization tools to communicate cybersecurity threats and risks (maps to PLO 4, PLO 5).

  3. CLO 3

    Implement predictive analytics and AI models to anticipate cyber incidents (maps to PLO 4, PLO 6).

  4. CLO 4

    Design dashboards that support decision-making in cybersecurity operations (maps to PLO 3, PLO 5).

Proposed syllabus

Weekly course schedule

The sequence below reflects the current 15-week syllabus and remains subject to institutional review.

  1. Week 1

    Introduction to Cyber Data Analytics and Visualization

  2. Week 2

    Data Sources in Cybersecurity (Logs, SIEM, IDS/IPS, Threat Feeds)

  3. Week 3

    Data Cleaning, Integration, and Preprocessing for Cyber Data

  4. Week 4

    Statistical Foundations for Cyber Data Analysis

  5. Week 5

    Exploratory Data Analysis in Python and R

  6. Week 6

    Machine Learning Techniques for Threat Detection

  7. Week 7

    AI and Deep Learning Applications in Cybersecurity

  8. Week 8

    Midterm Exam and Practical Project Proposal

  9. Week 9

    Visualization Tools: Power BI, Tableau, Kibana, and Grafana

  10. Week 10

    Network Traffic Visualization and Anomaly Detection

  11. Week 11

    Dashboard Development and Performance Metrics

  12. Week 12

    Predictive Modeling for Risk Forecasting

  13. Week 13

    Case Studies: Data-Driven Threat Intelligence

  14. Week 14

    Final Project Presentations

  15. Week 15

    Final Exam and Course Wrap-Up

Evaluation

Assessment and grading

  • Data Analytics Project – 30%
  • Midterm Exam – 20%
  • Final Exam – 20%
  • Weekly Assignments and Labs – 20%
  • Participation and Discussion – 10%
A = 90–100%B = 80–89%C = 70–79%F = Below 70%

Course resources

Required texts and materials

  • Conti, G., & Raymond, D. R. (2017). Data-driven security: Analysis, visualization, and dashboards. Wiley.
  • Lau, C., & Lee, J. (2023). Cybersecurity data science: Best practices in data security and analytics. Springer.
  • Supplemental datasets, scripts, and visualization tools will be provided via the course LMS.