M.Tech. in Computer Science and Engineering
This is a two year post-graduate engineering programme supported by multidisciplinary faculty. Data is the new “oil”. Read more about this important field here.
This is a unique program for those who are interested in shaping and creating a future world where AI and Machine Learning, Natural Language processing, and business intelligence are providing opportunities and competitive advantages. It will empower the students with Data Science skills and competencies. The students will acquire the following skills: Research Design, Data Cleansing, Data Engineering, Data Mining and Exploring, Data Visualization, Information Analytics, Ethics and Privacy, Statistical Analysis, Machine Learning, Communicating Results, and many others.
Eligibility
BTech/BE, or MCA/MSc in CS/IT/CE/MATH/STAT streams, or MBA (or equiv.), with at least 55% aggregate marks or equivalent CGPA. (B.Sc. degree with an exceptional record and/or work experience can be considered). Candidates will be shortlisted based on academic credentials and Statement of Purpose (SoP). Shortlisted candidates will be invited for Personal Interview leading to the final unified selection. Candidates with a sound background in Mathematics and/or Statistics with at least basic coding knowledge are preferred
You may contact the Dean’s office for the program related questions ([email protected] or by phone).
All admission-related queries may be directed to: [email protected]
Phone: 0674 – 2377 806
Course Duration and Structure
The Curriculum is a blend of machine learning and programming and business-oriented subjects. The program includes an internship and a capstone project to foster interaction with the data science community and offers opportunities for applying data science knowledge.
Opportunities for various workshops such as SAS, Linux, Hadoop, Python, R will be available.
Syllabus
The course has 4 semesters spread over 2 years. Core courses and electives offered are as follows.
Year One |
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Semester I |
Semester II |
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Statistical Foundations for Data Science |
Artificial Intelligence |
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Programming for Data Science |
Machine Learning |
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Data Mining & Data Warehousing |
Elective I |
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Linear Algebra and Matrix Computation |
Computer Vision and Image Processing |
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Data Structures and Algorithm |
Optimization Techniques |
Year Two |
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Semester III |
Semester IV |
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Elective II |
Thesis |
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2. Elective III |
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Big Data Analytics |
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Deep Learning |
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Capstone Project |
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Electives
- Natural Language Processing
- Cryptography and Network Security
- Cloud Computing
- Internet of Things
- Social Media Mining
- Information Retrieval
- Pattern Recognition
- Advance Algorithm
- Multivariate Data Analysis
- Programming Model for Multi-core GPU
- Quantum Computing
- Healthcare Data Analysis
- Business Data Analysis
- Financial Data Analysis
- Time Series Data Analysis
- Causal Inference
The above list of electives is open and may be offered if sufficient demand exists.