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

Assessment

The class is assessed 100% by coursework.

Lecturer

Joseph El Gemayel

The aim of this module is to endow students with:

an understanding of the new challenges posed by the advent for big data, as they refer to its modelling, storage, and access;
an understanding of the key algorithms and techniques which are embodied in data analytics solutions.

After completing this module participants will be able to:

understand the fundamentals of Python to enable the use of various big data technologies;
understand how classical statistical techniques are applied in modern data analysis;
understand the potential application of data analysis tools for various problems and appreciate their limitations.

Introduction to Python;
Quantitative methods for data analysis and knowledge extraction including classification, clustering, association rules, Bayesian approaches, decision trees.

Syllabuses - PG