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M.Tech. in Data and Computational Sciences

Data and Computational Sciences

Courtesy: Google Images


Introduction
Data Science is the art of generating insight, knowledge and predictions by processing of data gathered  about a system or a process. Computational Science is the art of developing validated (simulation) models in order to gain a better understanding of a phenomenon (system’s or processes). Computational sciences focus on development of causal models using latent patterns in the observed data, rather than only extracting patterns or knowledge from data by statistical methods.

Objective of the Program
To produce  professionals with deep knowledge and innovative analytical and computational research skills to handle problems in a variety of domains including governance, finance, security, transportation, healthcare, energy management, agriculture, population studies, weather prediction, economics, social sciences, predictive maintenance, structural health monitoring, smart manufacturing and computational structural biology.

Expected Graduate Attributes (M.Tech.)
1. Skill set to clean, process, analyse, manage and handle security and privacy aspects of structured and unstructured data.
2. Ability to identify, design and apply appropriate pattern recognition and data mining methods for generating relevant insight from data
3. Knowledge and capability to develop and apply machine learning techniques for data driven modelling.
4. Ability to develop models and simulation schemes based upon domain knowledge in chosen domains and possible combination with data driven models
5. Capability to follow uniquely interdisciplinary approach for solving problems, using knowledge of mathematics, statistics, computing and one or more selected domains among  physics, chemistry, biology and engineering sciences.
6. Skill to use and design appropriate visualisation techniques for representation and presentation of insights and solutions.
7.  Ability to innovate and contribute towards next generation data driven technology development. 
8. High quality technical communication skills.
9. Appreciation and adherence to norms of professional ethics.
10. Ability to plan and manage technical projects.
   
Learning Outcome
1. Strong Understanding of fundamentals of Data Mining, Machine Learning,  Modelling & Simulation, Optimization and Numerical Techniques
2. Basic understanding of Cryptographic and Blockchain Techniques.
3. Knowledge about basics and use of visual analytics.
4. Skill set to develop applications using Big Data
5. Advanced analytical and data driven modelling and simulation skills to address technological challenges in one or more specialised knowledge  domains like physics, chemistry, biology and engineering sciences.
6. Demonstrate skills to communicate scientific ideas and/or application systems.
7. Acquire project management skills.