Page 73 - PG-Handbook23-24-finale

WOC7017 Big Data Processing

Course Learning Outcomes
At the end of the course, students are able to:
1. Explain the concepts of big data technologies
2. Apply parallel processing techniques for processing big data.
3. Evaluate the suitability of different processing techniques for big data processing

Synopsis of Course Content
It becomes more and more difficult to handle the growing amount of data with traditional data processing
methods. There are many parallel processing frameworks and systems have been introduced such as
MapReduce, Hadoop, Pig, Hive, Spark and Twister. Many of these frameworks and systems can handle
different kinds of big data problems. This course will review and analyse various processing systems,
architectures, frameworks, programming languages and programming models and their capabilities for
large-scale data. This course will also analyze the advantages and disadvantages of these processing
paradigms within the scope of the big data.

Evaluation and Weightage
Continuous Assessment : 60%
Final Examination : 40%

WOC7018 Requirements Engineering

Course Learning Outcomes
At the end of the course, students are able to:
1. Describe current techniques used in core activities in software requirements engineering.
2. Use suitable techniques and tools to develop software requirements specification to fulfill user
requirements.
3. Evaluate relevant research issues in improving requirements engineering process.

Synopsis of Course Content
This course covers core activities in requirements engineering process such as requirements elicitation,
validation, management and negotiation and techniques, tools and methods for supporting those
activities. It also discusses and explores relevant research issues in areas such as requirements
prioritization, impact analysis, process change management and requirements traceability.

Evaluation and Weightage
Continuous Assessment : 60%
Final Examination : 40%