Data Science and Computational Intelligence course aims to respond the demand for data scientists with the skill to develop innovative computational intelligence applications, capable of analyzing large amounts of complex data to inform business decisions and market strategies.
Admission Eligibility
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.
English Requirement
The students with valid IELTS Report Forms with a 6.5 overall band or equivalent are eligible to apply.
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. |
| Data Management Systems : 15 credit |
| Database modelling, 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 model, Text classification, Document clustering, Link analysis, Multimedia information retrieval, etc. |
| Introduction to Statistical Methods for Data Science : 15 credit |
| 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 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 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 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. |
| Advanced Machine Learning : 15 credit |
| Gaussian processes, Dirichlet processes, Graphical models, Fuzzy sets, Adaptive and hybrid fuzzy systems, Evolutionary algorithms |
| Individual Research Project Preparation :15 credit |
| Research skills, Research methodology, Reporting, Legal, Ethical and Social context |
| Computing Individual Research Project : 60 credits |
| 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. |