Colleges offering MSc Data Science and Computational Intelligence under Coventry University, UK
The MSc Data Science and Computational Intelligence program, affiliated with Coventry University, UK, seeks to meet the demand for data scientists who can create cutting-edge applications for computational intelligence and analyze vast amounts of complex data to guide business choices and marketing practices.
This course focuses on automatic big data processing and information retrieval using evolutionary computing, neural networks, and machine learning. Similarly, 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 extensively 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 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
Assessment
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.
Fee Structure
| PARTICULAR | AMOUNT (NPR) |
|---|---|
| Admission Fee | 25,000/- |
| University Registration Fee | (GBP-1270) RS 2,15,900/- |
| Semester 1 Fee | 1,25,000/- |
| Semester 2 Fee | 1,25,000/- |
| Semester 3 Fee | 1,25,000/- |
| Total Amount | 6,15,900/- |
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.
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.