MSc Data Science and Computational Intelligence

MSc Data Science and Computational Intelligence

  • Fee:NPR 900,500

MSc Data Science and Computational Intelligence program, affiliated to Coventry University, UK, seeks to meet the demand for data scientists who can create cutting-edge applications for computational intelligence and analyse vast amounts of complex data to guide business choices and marketing practices.

This course's overarching focus is automatic big data processing and information retrieval using evolutionary computing, neural networks, and machine learning. In a similar vein, it seeks to address how to use cutting-edge machine learning algorithms to analyze large datasets, evaluate the statistical significance of data mining results, and carry out advanced data mining activities. Furthermore, significant frameworks such as Hadoop Map Reduce, Spark, applications of relational databases, and NoSQL databases will be covered in class, along with simple-to-use yet potent development tools like Python, R, and Matlab.

Main Aim of MSc Data Science and Computational Intelligence

  • Deliver advanced theoretical and practical subjects  across a range of specialist areas in data science and computational
  • the intelligence which is greatly demanded in a wide range of research and industrial applications;
  • Enable students to enhance their analytical, problem solving, critical communication, and presentation skills in the context
  • of their taught modules and develop the ability to analyze, evaluate and model complex problems involving large amounts of data;
  • Advance the skills and knowledge acquired through previous study and experience in cutting-edge research and technologies and enhance students’ transferable and professional skills and, thereby, their employment prospects;
  • Provide specialist skills and in-depth knowledge essential for graduates to develop and adapt to the challenges in the field of data science;
  • Enable students to analyze and critique the central and current research problems in data science and computational
  • intelligence;
  • Enable students to operate as effective independent researchers and/or consultants in their chosen specialized aspect of the course;
  • Enhance the awareness of the professional, legal, ethical, and social issues along with commercial risk and management in the role of a data science professional.
  • Enable students to adapt to future changes in technology in data science and computational intelligence areas. 

Salient Features


Numerous methods, some of which may change based on the module, will be used to evaluate this course. Coursework, essays, projects, group projects, and formal examinations are all examples of assessment procedures.

The Coventry University's assessment system seeks to ensure that our courses are fairly evaluated and enables us to track students' advancement toward reaching the desired learning outcomes.


  • 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.

Curricular Structure

Title Credit Value Description
Machine Learning 15 Applications of machine learning, Supervised / Unsupervised learning, Linear regression, Logistic regression, Regularisation, Support vector machine, Decision trees, Reinforcement learning, etc.
Data Management Systems 15 Database modelling, Relational models, Big-data, NoSQL databases, Database programming, Distributed databases, Transaction management, etc.
Intelligent Information Retrieval 15 Search engines, Web crawlers, Query processors, Boolean model, Text classification, Document clustering, Link analysis, Multimedia information retrieval, etc.
Introduction to Statistical Methods for Data Science 15 Use of 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 Analytical review of database system and big data, Traditional database concepts for structured data, Big data methodologies for structured and unstructured data sets,
Big data analysis using examples from real life case studies and datasets. Big data processing and predictive frameworks. Data visualisation tools to support decision-making.
Artificial Neural Networks 15 Supervised and unsupervised neural networks, Static and temporal neural networks, Deep neural networks, Hybrid and modular neural networks, Various neural networks, and their applications.
Advanced Machine Learning 15 Gaussian processes, Dirichlet processes, Graphical models, Fuzzy sets, Adaptive and hybrid fuzzy systems, Evolutionary algorithms
Individual Research Project Preparation 15 Research skills, Research methodology, Reporting, Legal, Ethical and Social context
Computing Individual Research Project 60 The project can be a solution to a practical industry requirement or focus on a research topic. It will require investigation and research as core activities, leading to analysis, final summations and insightful recommendations. The project will culminate in a comprehensive, thorough and professional report, documenting the approach, conduct and outcomes of the project, further supported with a critical review of the project conduct and management. It is intended that students will be given an opportunity to specialise in an area of interest, relevant and useful for future career prospects.