M.Tech. in Computational and Data Science

M.Tech. in Computational and Data Science

Program Flyer

Course structure: (effective from Aug 2018 batch)

  • Hard Core: 14 credits
  • Soft Core: 10 credits minimum (atleast three courses)
  • Dissertation: 28 credits
  • Electives: 12 credits (Students may credit CDS electives/soft core or other department courses)

Total: 64 credits

Hard Core Courses (14 credits): All are compulsory

  • DS 215 AUG 3:0 Introduction to Data Science (AC) 
  • DS 221 AUG 3:1 Introduction to Scalable Systems (VSS/YS) 
  • DS 284 AUG 2:1 Numerical Linear Algebra (PM)
  • DS 288 AUG 3:0 Numerical Methods (RB) 
  • DS 200 AUG 0:1 Research Methods (DP) – SOFT SKILLS COURSE [to be taken in the second year August term]

Soft Core Courses (10 credits): Minimum three courses out of ten below

  • DS 201 AUG 2:0 Bioinformatics (KS/DP)
  • DS 202 JAN 2:1 Algorithmic Foundations of Big Data Biology (CJ)
  • DS 211 AUG 3:0 Numerical Optimization (DS) 
  • DS 216 JAN 3:1 Machine learning for Data Science (VS)
  • DS 207 JAN 3:1 Introduction to Natural Language Processing (DAP)
  • DS 226 AUG 2:1 Introduction to Computing for AI & Machine Learning (SG)
  • DS 256 JAN 3:1 Scalable Systems for Data Science (YS) 
  • DS 285 JAN 3:1 Tensor Computations for Data Science (RB)
  • DS 289 JAN 3:1 Numerical Solution of Differential Equations (KA)
  • DS 290 AUG 3:0 Modelling and Simulation (SR)
  • DS 295 JAN 3:1 Parallel Programming (SV) 
  • DS 298 JAN 3:0 Random Variates in Computation (MV)
  • E0 261 JAN 3:1 Database Management Systems (JH)

Dissertation Project: DS 299  0:28  (0:4 Summer; 0:8 AUG; 0:16 JAN)

CDS Electives (all existing electives) / Elective courses offered by CDS Faculty. Check here for the  FULL LIST of COURSES

  • DS 244 JAN 2:1 Hardware-aware Scientific Computing (SG)
  • DS 255 JAN 3:1 System Virtualization (JL)
  • DS 261 AUG 2:1 Artificial Intelligence for Medical Image Analysis (PY)
  • DS 263 AUG 3:1 Video Analytics (RVB/AC)
  • DS 265 JAN 3:1 Deep Learning for Computer Vision (RVB)
  • DS 269 JAN 2:1  Computational Methods for Reacting Flows (AK)
  • DS 392 JAN 3:1 Environmental Data Analytics (DS)
  • DS 393 JAN 3:1 High-performance Computing for Quantum Modeling of Materials (PM)
  • DS 397 JAN 2:1 Topics in Embedded Computing 
  • E0 361 AUG 3:1 Topics in Database Systems (JH)