DESCRIPTION OF CORE MODULE S IN MASTER OF SCIENCE IN BIG DATA ANALYTICS
MSDA 9121 Statistical Computing 10 Credits
Modern statistics relies heavily on computing. This Module aims to teach a student not only using existing programs but also the principles on which they operate. In this Module, we cover different topics, including: numerical optimization and integration relevant to statistical calculations; linear algebra, simulation and Monte Carlo methods, including Markov chain Monte Carlo (McMC); bootstrapping; smoothing and density estimation; and other contemporary topics.
MSDA 9122 BIG DATA ESSENTIALS 20 Credits
Big Data embraces technology, decision-making and public policy supplying the technology is a fast-growing market. This subject aims at providing students with the knowledge of current challenges, methodologies and technologies in processing big data. Emphasis will be placed on the students’ understanding of the rationales behind the technologies and the students’ ability to analyze big data using professional software packages.
MSDA 9123 Big Data Analytics 20 Credits
Big Data Analytics Module will introduce to the students the overview applications of Big Data, fundamental platforms, such as Hadoop, Spark, and other tools, such as IBM System G for Linked Big Data. The Module also introduces several data storage methods and how to upload, distribute, and process them.
This shall include HDFS, HBase, KV stores, document database, and graph database. The Module will go on to introduce different ways of handling analytics algorithms on different platforms. Then, introduce visualization issues and mobile issues on Big Data Analytics. Students will then have fundamental knowledge on Big Data Analytics to handle various real-world challenges.
On completion of this module students will be competent in the application of Big Data frameworks for parallelized processing of data. This module will also equip students with the skills to perform analytics on big data problems using scalable machine learning algorithms and interpret the results.
MSDA 9124 Information Retrieval and Data Mining 15 Credits
This Module aims at introducing the area of Information Retrieval and at examining the theoretical and practical issues involved in designing, implementing and evaluating Information Retrieval systems. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and data mining are covered, the Module is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations. The topics to be covered in this Module are (but not limited to): Efficient text indexing, Boolean and vector-space retrieval models, Evaluation and interface issues, IR techniques for the web, including crawling, link-based algorithms, and metadata usage, Document clustering and classification, Traditional and machine learning-based ranking approaches.
MSDA 9125 Cloud Computing and Virtualization 10 Credits
This is an introductory Module to understand the concepts of Cloud Computing, Virtualization and Computer Networks in general. In this Module, the lessons are designed with minimal technical jargons to ensure anyone with little knowledge about computers or IT can gain a basic understanding of the above concepts. This Module may act as an essential prerequisite for those who aim to take any computing related training – specifically in Cloud Computing (e.g. Amazon AWS, Microsoft Azure), Cyber Security or any computer network related Module.
An introduction to the concepts and techniques of implementing cloud computing through the use of virtualization and distributed data processing and storage. Topics include operating system virtualization, distributed network storage, distributed computing, cloud models (IAAS, PAAS, and SAAS), and cloud security.
MSDA 9231 Distributed Systems 3 Credits
The module is designed to provide the students with a thorough understanding of issues involved in designing distributed systems. The aim of this module is to give students an understanding of the fundamentals and advanced aspects of distributed systems, and to provide students with the skills necessary to develop distributed systems. This module introduces the student to the distributed systems fundamentals, advanced and the issues facing their design and implementation. It provides the student with the skills necessary to develop distributed applications. The module also analyses advanced distributed systems issues such as: processes and scheduling, time, synchronization.