MSc Data Science and Computational Intelligence

MSc Data Science and Computational Intelligence programme aims to respond to the demand for data scientists with the skills to develop innovative computational intelligence applications, capable of analyzing large amounts of complex data to inform business decisions and market strategies.  

This course provides advanced theoretical and practical subjects across a range of specialist areas in data science and computational intelligence which are greatly demanded in a wide range of research and industrial application. The course preliminary focuses on enhancing the awareness with regards to professional, legal, ethical, and social issues along with commercial risk and management in the role of a data science professional and enables students to adapt to future changes in technology about data science and computational intelligence areas.

Affiliated with Coventry University, this MSc data science programme helps you learn alongside active researchers in pervasive computing, distributed computing, and innovative applications for interactive virtual worlds.

Throughout the program, you learn automatic big data processing and information retrieval through cutting-edge machine learning techniques and become capable of analyzing big datasets and performing advanced data mining tasks. You will be introduced to important frameworks including Hadoop Map Reduce, Spark, and NoSQL databases in combination with powerful development tools such as Python, Scala, Matlab, and R.

The overall aim of the MSc Data Science and Computational  Intelligence is to:

• Deliver advanced theoretical and practical subjects across a range of specialist areas in data science and computational 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 MSc 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.

Entry Requirements

• Students who have graduated in a relevant computing field with a score of 60%.
• However, at least one year experience in relevant field should be accounted for.
• For students graduated from Bachelors of Science a least 1 year of experience in IT field is required.

Course Modules

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 system 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 visualisation 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 and professional report, documenting comprehensively, thoroughness of the project, critical review of the project conduct and management.

Fee Structure