MSc Data Science and Computational Intelligence at Coventry University, MSc Data Science and Computational Intelligence Colleges in Nepal

MSc Data Science And Computational Intelligence

Affiliated to: Coventry University

About Course

Course Title : MSc Data Science And Computational Intelligence
Category/Level : Masters
Course Duration (months) : 24 months
Affiliated to : Coventry University

Course Description:

MSc Data Science and Computational Intelligence, in collaboration with Coventry University UK, 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.

The course addresses the critical industry demand for professionals who can harness complex data and develop advanced computational intelligence applications to drive strategic decision-making, ensuring graduates are positioned at the forefront of this high-growth field. It uniquely prepares graduates with not only the technical skills to manage and analyze big data but also the professional and ethical framework to lead and innovate responsibly in the global data science landscape.

Admission info

Eligibility
- Students must have obtained at least 50% or equivalent marks in the undergraduate level.
- Must fulfil the English requirement as per the requirement of the university.
- Accepted Undergraduate Degrees: Computer Science or relevant,Computer Engineering, Electronics Engineering, Science (Physics/Mathematics)
- Other undergraduate degree’s may also considered depending upon recent work experience.

Syllabus

Curricular 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.