Last Date to Apply : May 2, 2021
All courses are AICTE approved
GATE Candidates - Eligible for DIRECT Admissions
Non-GATE candidates - Online Interview Process
(NO Entrance exam required this year)
This program is designed to enable learners to master critical skills such as mathematical modeling, machine learning and artificial intelligence. On the whole, the Master’s Program is committed to produce engineers with excellent creative capabilities and caliber to solve real life problems pertaining to industry requirements and advance their career as a Data Scientist or Data Engineer, in tune with the objectives envisioned by Amrita Vishwa Vidyapeetham.
Students who have completed the M.Tech in Data Science Program will be able to:
Duration : Two years
Research is an integral part of the Data Science and numerous research opportunities and career progression are available for which the program will serve the foundation. They can engage in research across a wide range of data science areas, combining theoretical work with an application focus on areas like health care domain, social media mining, cyber and business analytics.
Data Science Career Opportunities
A Data Scientist, according to Harvard Business Review, “is a high-ranking professional with the training and curiosity to make discoveries in the world of Big Data”. Therefore it comes as no surprise that Data Scientists are coveted professionals in the Big Data Analytics and IT industry. Demand for data scientists is booming .Fueled by big data and AI, demand for data science skills is growing exponentially. Coding skills in R/Python/Java clubbed with knowledge of statistics and applied mathematics, working knowledge of Hadoop , Spark,SQL, NoSQL, machine learning and neural networks, text processing, text analytics, proficiency in deep learning frameworks such as TensorFlow, Keras, Pytorch will fetch big career opportunities in Data Science.
Participants need to take certain number of Courses, Minor Project and Dissertation. The coursework requirement for the program would consist of a set of core courses and electives. Core courses are compulsory for all participants, while electives can be chosen based on individual learning preferences.
First Semester | ||||
Course Code | Type | Course | L T P | Credits |
---|---|---|---|---|
19MA610 | FC | Linear Algebra and Optimization Techniques | 3 0 0 | 3 |
19DS601 | FC | Data Structures and Algorithms | 3 0 1 | 4 |
19DS611 | SC | Big Data Mining | 3 0 1 | 4 |
19DS612 | SC | Statistical Learning | 3 0 0 | 3 |
19DS613 | SC | Computational Intelligence | 3 0 0 | 3 |
19DS614 | SC | Data Preparation and Analysis | 2 0 1 | 3 |
19HU601 | HU | Amrita Values Program* | 0 | P/F |
19HU602 | HU | Career Competency I* | 0 | P/F |
Total Credits | 20 | |||
*Non-credit course |
Second Semester | ||||
Course Code | Type | Course | L T P | Credits |
---|---|---|---|---|
19DS615 | SC | Machine Learning | 3 0 1 | 4 |
19DS616 | SC | Deep learning | 3 0 1 | 4 |
19DS617 | SC | Time Series Analysis and Forecasting | 2 0 1 | 3 |
E | Elective-I | 3 0 0 | 3 | |
E | Elective-II | 3 0 0 | 3 | |
19RM600 | SC | Research Methodology | 2 0 0 | 2 |
19HU603 | HU | Career Competency II | 0 0 2 | 1 |
Total Credits | 20 |
Third Semester | ||||
Course Code | Type | Course | L T P | Credits |
---|---|---|---|---|
E | Elective-III | 3 0 0 | 3 | |
E | Elective-IV | 3 0 0 | 3 | |
19DS798 | P | Dissertation | 8 | |
Total Credits | 14 |
Fourth Semester | ||||
Course Code | Type | Course | L T P | Credits |
---|---|---|---|---|
19DS799 | P | Dissertation | 12 | |
Total Credits | 12 |
Foundation Core (FC) | ||||
Course Code | Course | L T P | Credits | |
---|---|---|---|---|
19MA610 | Linear Algebra and Optimization Techniques | 3 0 0 | 3 | |
19DS601 | Data Structures and Algorithms | 3 0 1 | 4 | |
Subject Core (SC) | |||
Course Code | Course | L T P | Credits |
---|---|---|---|
19DS611 | Big Data Mining | 3 0 1 | 4 |
19DS612 | Statistical Learning | 3 0 0 | 3 |
19DS613 | Computational Intelligence | 3 0 0 | 3 |
19DS614 | Data Preparation and Analysis | 2 0 1 | 3 |
19DS615 | Machine Learning | 3 0 1 | 4 |
19DS616 | Deep learning | 3 0 1 | 4 |
19DS617 | Time Series Analysis and Forecasting | 2 0 1 | 3 |
Electives (E) | |||
Course Code | Course | L T P | Credits |
---|---|---|---|
19DS701 | Natural Language Processing | 3 0 0 | 3 |
19DS702 | Information Retrieval | 3 0 0 | 3 |
19DS703 | Semantic Web | 3 0 0 | 3 |
19DS704 | Data Visualization | 3 0 0 | 3 |
19DS705 | Networks and Spectral Graph Theory | 3 0 0 | 3 |
19DS706 | Video Analytics | 3 0 0 | 3 |
19DS707 | Content Based Image and Video Retrieval | 3 0 0 | 3 |
19DS708 | 3D Modeling for Visualization | 3 0 0 | 3 |
19DS709 | Computer Vision | 3 0 0 | 3 |
19DS710 | Image Analysis | 3 0 0 | 3 |
19DS711 | Reinforcement Learning | 3 0 0 | 3 |
19DS712 | Bio Informatics | 3 0 0 | 3 |
19DS713 | Data Compression | 3 0 0 | 3 |
19DS714 | Modeling and Simulation | 3 0 0 | 3 |
Electives (E) | |||
Course Code | Course | L T P | Credits |
---|---|---|---|
19DS715 | Recommender System | 3 0 0 | 3 |
19DS716 | Data Warehouse and Data Mining | 3 0 0 | 3 |
19DS717 | Web Analytics and Development | 3 0 0 | 3 |
19DS718 | Text Analytics | 3 0 0 | 3 |
19DS719 | Blockchain Technology | 3 0 0 | 3 |
19DS720 | Sensor Networks and IoT | 3 0 0 | 3 |
19DS721 | Predictive Analytics for Internet of Things | 3 0 0 | 3 |
19DS722 | Data Intensive Computing | 3 0 0 | 3 |
19DS723 | Parallel and Distributed Computing | 3 0 0 | 3 |
19DS724 | Pervasive Computing | 3 0 0 | 3 |
19DS725 | Data Security and Access Control | 3 0 0 | 3 |
19DS726 | Cloud Computing &Security in the Cloud | 3 0 0 | 3 |
19DS727 | Embedded Systems for Data Analytics | 3 0 0 | 3 |
Students are allowed to choose the electives offered under M. Tech (DS) |
Centre for International Programs facilitates foreign internship with scholarship and higher education. Students can even opt for dual degree programs.
Visit Department of Computer Science and Engineering (Bengaluru Campus) Website
If you wish to know more about the course please mail to,
mtech@amrita.edu
2019 |