CS989 - Big Data Fundamentals

Credits

Level

Semester

Semester 1

Availability

Possible elective

Prerequisites

N/A

Learning Activities Breakdown

Lectures: 10 | Labs: 10
Homework/Private Study: Assignments: 80

Items of Assessment

Assessment

The class is assessed 100% by coursework.

Lecturer

Joseph El Gemayel

The objectives of the module are to:

Provide students with a comprehensive understanding of the challenges and opportunities presented by big data, including its modelling, storage, and access.
Equip students with a critical understanding of the theoretical foundations and practical applications of key algorithms and techniques used in data analytics.

After completing this module, students will be able to:

Apply fundamental Python programming skills to engage with a range of big data technologies and tools.
Explain and evaluate the use of classical statistical techniques in modern data analysis contexts.
Assess the suitability of different data analysis methods and technologies for specific problem domains, considering both their capabilities and limitations.

Fundamentals of Python programming for data analysis, including data structures, libraries, and scripting for automation.
Overview of supervised learning techniques for classification and regression, and unsupervised models for clustering. Emphasis on both theoretical foundations and practical application.

Syllabuses - PG