Course Overview
Msc Data Science and Computational Intelligence course aims to respond to the demand for data scientists with the skill to develop innovative computational intelligence applications, capable of analyzing, offered as a leading data science course.
Eligibility Requirements
Honours degree or equivalent in relevant subjects like Statistics.
Mathematics.
Computer Science.
Physics.
Engineering.
etc. Alternatively.
an unclassified data science degree with relevant field experience is accepted..
Course Structure
Machine Learning - 15 credit
Applications of machine learning, supervised / unsupervised learning, linear regression, logistic regression, regularisation, support vector machine, decision trees, reinforcement learning, etc.
Artificial Neural Networks - 15 credit
Supervised and unsupervised neural networks, static and temporal neural networks, deep neural networks, hybrid and modular neural networks, various neural networks, and their applications.
Introduction to Statistical Methods for Data Science - 15 credit
Use of a range of statistical distributions like binomial, Poisson, uniform, normal, exponential, gamma, etc. Multivariate distributions, central limit theorem, hypothesis testing, bayesian inference, regression models, etc.
Big Data Management and Data Visualisation - 15 credit
Analytical review of database systems and big data, traditional database concepts for structured data, big data methodologies for structured and unstructured data sets, big data analysis techniques and tools, real-life case studies and analysis, big data technologies for knowledge extractions, and data visualization tools to support decision-making.
Data Management Systems - 15 credit
Database modeling, relational models, big data, NoSQL databases, database programming, distributed databases, transaction management, etc.
Intelligent Information Retrieval - 15 credit
Search engines, web crawlers, query processors, boolean models, text classification, document clustering, link analysis, multimedia information retrieval, etc.
Advanced Machine Learning - 15 credit
Gaussian processes, Dirichlet processes, graphical models, fuzzy sets, adaptive and hybrid fuzzy systems, evolutionary algorithms, etc.
Individual Research Project Preparation - 15 credit
Research skills, research methodology, reporting, legal, ethical, and social context.
Computing Individual Research Project - 60 credit
Prepare a project to solve a practical industry problem. Literature and research for activities, leading to analysis, final output, and technical recommendations. Evaluation of components through a professional report, documenting comprehensively, thoroughness of the project, critical review of the project conduct, and management.