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MSc Data Science and Computational Intelligence

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

 

·        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.  

On successful completion of the course student will be proficient in:

  • Data analysis and analytics
  • Statistical analysis
  • Machine learning algorithms
  • Artificial neural networks
  • Deep learning
  • Big data management systems
  • Research skills for advanced data science and computational intelligence

This course will be delivered in the following structure:

TitleCredit ValueDescription
Machine Learning15Applications of machine learning, Supervised / Unsupervised learning, Linear regression, Logistic regression, Regularisation, Support vector machine, Decision trees, Reinforcement learning, etc.
Data Management Systems15Database modelling, Relational models, Big-data, NoSQL databases, Database programming, Distributed databases, Transaction management, etc.
Intelligent Information Retrieval15Search engines, Web crawlers, Query processors, Boolean model, Text classification, Document clustering, Link analysis, Multimedia information retrieval, etc.
Introduction to Statistical Methods for Data Science15Use 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 Visualisation15Analytical 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 Networks15Supervised 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 Learning15Gaussian processes, Dirichlet processes, Graphical models, Fuzzy sets, Adaptive and hybrid fuzzy systems, Evolutionary algorithms
Individual Research Project Preparation15Research skills, Research methodology, Reporting, Legal, Ethical and Social context
Computing Individual Research Project60The 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.

Softwarica Fee Structure for MSc Data Science and Computational Intelligence

ParticularAmount (NPR)
Admission50,000/-
University Registration Fee(GBP 1270) RS 1,90,500/-
Semester 1 Fee2,20,000/-
Semester 2 Fee2,20,000/-
Semester 3 Fee2,20,000/-
Total Amount 9,00,500/-

Entry Requirements:

  • 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

Notes:

  1. University Registration Fee may vary in the upcoming years as per the university’s policy.
  2. University Registration Fee may vary in the upcoming years as per the Nepal government’s tax policy.
  3. The University Registration Fee is subject to change as per the prevailing foreign exchange rate set by the commercial bank. The University Registration Fee in the fee structure is set at 1£= NRS 150.
 

Launching offer

  • Flat 35% scholarship on semester fees