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This course introduces students to Big Data, decision analytics, and the use of Big Data for decision making with an emphasis on security. The course includes an overview of Big Data, data collection, data storage, data security, and regularity and compliance issues. The basic analytics will cover data analysis, prediction, classification, data anomaly, knowledge and pattern discovery, and simulation and decision making. The course will also study a number of real-world scenarios related big data use in security intelligence.

Big Data represents information from big and often dynamic data sets in variety of forms collected from multiple autonomous or connected sources. Current best practices, compliance and regulatory requirements have dramatically increased the production and retention of data.

Methods used for working with Big Data are very different from traditional analysis. Big Data caries a lot of noise that needs to be filtered out, it is dynamic, and can be untrustworthy. Traditional tools and approaches are either not efficient or not capable to respond to the need of smart security intelligence and immediate incident management and response.

We will define what structured, semi structured and unstructured data is and discuss different approaches to extract useful and context aware security intelligence. Different Technologies for analysing Big Data will be evaluated during the course. We will look at some of the tools used over the course of the semester.

Study Level


Offering Terms

Semester 2



Indicative contact hours


Course Outline

To access course outline, please visit:


Pre-2019 Handbook Editions

Access past handbook editions (2018 and prior)

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