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Evaluation and Weightage
                Continuous Assessment    :  50%

                Final Examination        :  50%



               WQD7006        Machine Learning for Data Science


               Learning Outcomes
               At the end of this course, students are able to:
               1. Explain the concepts and techniques for machine learning.
               2. Identify appropriate machine learning techniques for various datasets.
               3. Evaluate practical solutions to common problems in machine learning.

               Synopsis of Course Content
               This course introduces fundamental concepts and techniques for machine learning. It covers topics
               such as linear and logistics regression, decision trees, support vector machines, and reinforcement
               learning.

               Evaluation and Weightage
                Continuous Assessment    :  50%
                Final Examination        :  50%



               WQD7007        Big Data Management



               Learning Outcomes
               At the end of this course, students are able to
               1. Explain the processes in data pipeline
               2. Discuss database concepts and technologies for big data storage and retrieval
               3. Apply  appropriate models, tools, and technologies  to implement storage, search and retrieval
                   systems for large-scale structured and unstructured information systems.
               4. Analyse data provenance and data trustworthiness, and its role in sharing and reuse of data.

               Synopsis of Course Content
               This course prepares students to deal  with  large-scale collections of data  as objects to be  stored,
               searched  over, selected, and transformed for use and reuse. It  examines the  underlying principles
               and  technologies  used  to  capture  data,  clean  it,  contextualize  it,  store  it,  and  access  it  for  a
               repurposed use. Data provenance is also examined to determine the trustworthiness of data.

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


               WQD 7008       Parallel and Distributed Computing



               Learning Outcomes
               At the end of this course, the students are able to:
               1. Recognize the underlying principles of parallel and distributed computing.
               2. Determine the fundamental paradigms of parallel and distributed computing.
               3. Identify  the  issues  and  problems,  together  with  the  solutions  in  implementing  parallel  and
                   distributed systems.
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