Academics
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.
Program Highlights
- The curriculum covers areas that prepare you for most lucrative careers in the space of Data Science, Data Engineering and Advanced Analytics. The programme provides extensive knowledge on most popular data science techniques such as mathematical modeling, machine learning, artificial intelligence, product development and scripting languages.
- Benefit from Case Studies, Simulations, Virtual Labs & Remote Labs that allow learners to apply concepts to simulated and real-world situations. Tools & Technologies covered include Apache Spark, Apache Storm for Big Data Systems/ Real time Processing, Tableau for data visualization, Tensor flow for Deep Learning and various packages within Python for data processing, machine learning and data visualization.
- The programme offers core courses such as Data Structures and Algorithms, Foundation of Data Science, Big Data Mining, Computational Intelligence, Data Preparation and Analysis, Statistical Learning, Machine Learning, Deep Learning and Time Series Analysis and Forecasting. Industry experts in each stream will be invited to give sessions on practical implications of classroom learned concepts in business use cases.
- Integrated Industry approach for problem solving in the curriculum and the delivery of the course content.
- The Dissertation enables students to apply concepts and techniques learnt during the programme.
Program Outcomes
Students who have completed the M.Tech in Data Science Program will be able to:
- Develop in depth understanding of the key technologies in data science and business analytics such as structured/unstructured data mining, machine learning, visualization techniques, predictive modeling, and statistics.
- Practice problem analysis and decision-making.
- Interpret data, extract meaningful information, and assess findings.
- Effectively communicate data science-related information effectively in various formats to appropriate audiences.
- Apply quantitative modeling and data analysis techniques to transform findings from data resources into actionable business strategies.
- Gain practical, hands-on experience with statistics programming languages and big data tools through coursework and applied research experiences.
- Develop an ability to contribute by research and innovation to solve engineering problems.
Program Curriculum
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 | |||
Total Credits: 66
| 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) | |||
How to Pursue Research Path after Completing this Program ?
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.
Advantages in Terms of Placements
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.
Alumni Speaks
This course helped me to grasp both mathematical and programming facets of data science and gave a broad over view of the data science field itself. The exposure to R, STATA and real time case studies enabled us to be industry relevant. Dissertation in the final semester in the area of text analytics has helped me develop expertise in the domain. The course also honed my skills on the software side by introducing us to latest big data technologies.
Manju Priya A. R. | Software Designer @ MicroFocus
I recommend this course to all data enthusiasts who like to convert skill sets from a traditional statistician/ analyst to a data scientist and understand the insights systematically rather than following the commercial analytical software blindly. It is a very good breakthrough for career advancement supported by a reputed master’s degree from Amrita Vishwa Vidyapeetham.
Sarath Sasidharan | Technical Lead,Sony India Software Center Pvt Ltd, Bangalore
Studying in Data Science course in Amrita has widened my perspective and has given me an idea of importance of the data and its necessity in today’s day and age.The faculties are very helpful and always present to clear our doubts in order to lead us in the right path.
Shrihari Rao | Consultant, Siemens
Industry Experts Testimony
M.Tech Data Science programme at Amrita is a great way to learn new age essential skills for a career change or to advance in your own field. I am glad to be part of the teaching faculty for the core course ‘Data Visualization’.
Dr. Anand Lakshmanan | Ex- Apple Design Engineer, Founder and CEO of SIRPI
With current disruptive technology era, Data science programme play a major role in shaping the future. M.Tech Program in data science @Amrita is one of the promising courses which help the students to shape their career in data science. I am happy to be part of this program.
Dr. Rajkumar Elagiri Ramalingam | Senior Architect, Robert Bosch Engineering and Business Solutions
Year-wise Curriculum & Syllabus
|
2019 |
