CS982 - Big Data Technologies

TIMETABLE	TEACHING MATERIAL
Credits	20
Level	5
Semester	Semester 1
Availability	Possible elective
Prerequisites	N/A
Learning Activities Breakdown	Lectures: 20 hours \| Labs: 20 hours Homework / Private Study: 160
Assessment	One individual assignment worth 50%, and a final 2-hour examination worth 50%.
Lecturer	Joseph El Gemayel, William Bell

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;
an exposure to a number of different big data technologies and techniques, to show how they can achieve efficiency and scalability while also addressing design trade-offs and their impacts.

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;
be familiar with a number of different cloud NoSQL systems and their design and implementation, showing how they can achieve efficiency and scalability while also addressing design trade-offs and their impacts;
be familiar with the Map-Reduce programming paradigm.

Introduction to Python;
Quantitative methods for data analysis and knowledge extraction including classification, clustering, association rules, Bayesian approaches, decision trees;
Overview of various NoSQL cloud storage systems such as document stores like MongoDB, column stores like Cassandra and graph databases like Neo4j;
Distributed data processing with Hadoop and MapReduce.

Syllabuses - PG