Interdisciplinary School Of Scientific Computing

Estd. - 1994
Savtribai Phule Pune University
This revised syllabus introduced in July-2023 offers the MACHINE LEARNING course for three semesters during the M.Sc. (Scientific Computing) programme.
This syllabus is NEP 2020 compliant.

M.Sc. - Scientific Computing - Syllabus

    Semester I

  • Programming Languages and principles I
  • Software Engineering
  • Mathematics for Scientific Computing
  • Research Methodology for Scientific Computing
  • Linear Algebra
  • Computational Laboratory I (Project)

    Semester II

  • Programming Languages and Principles II
  • Operating System Concepts
  • CBCS Elective - I
  • Numerical Computing - 1
  • Computational Laboratory II (Continued from Sem I)
  • Field Project or On-Job-Training

    Semester III

  • Network Concepts
  • Grid / High Performance Computing (HPC).
  • CBCS Elective II
  • Numerical Computing II
  • Python for Scientific Computing
  • Optimization Concepts

    Semester IV

  • R & D/Industrial Project
  • MOOC course
Detailed updated syllabus (July 2023) (Download PDF)
Apart from the electives listed here M.Sc. (Scientific Computing) students can take elective's from M.Sc./MCA - Computer Science, M.Tech - Modeling & Simulation, etc.

Electives

  • EL-1 : Parallel Computing and Grid Computing
    • Introduction Solving Problem in parallel Structure of parallel computers Programming parallel computers Case Studies Grid Computing
  • EL-2 : Application of Computers to Chemistry
    • Computational Chemistry, Fundamentals of Chemistry, Molecular Representations and Search Molecular Graphics and fitting Force Field (FF) Methods Classical energy minimization techniques Conformational Analysis, Semi-empirical QM calculations Molecular Docking Molecular Descriptors Quantitative Structure Activity, Relationship Futuristic modeling techniques
  • EL-3 : Statistical Computing
    • Introduction to statistical computing, Random Number Generation, Monte Carlo Methods, Non-linear Statistical Methods, Multiple Linear Regression Analysis
  • EL-4 : Computer Application if Physics
    • Monte Carlo Methods, Numerical Solutions of Schrodinger equations, Electronic Structure Calculation on simple solids, Classical Molecular Dynamics
  • EL-5 : Biological Sequence Analysis
    • Analysis of DNA and Protein sequence, Sequence alignment, Fragment assembly, Genome sequence assembly, Neural network concepts and secondary structure prediction Probabilistic models, Evolutionary analysis
  • EL-6 : Modeling of Biological Systems
    • Concepts and principles of modeling. Limitations of models, Models of behavior, Modeling in Epidemiology and Public Health SIR models
  • EL-7 : Artificial Intelligence
    • Introduction to Artificial Intelligence Game playing Knowledge representation using predicate logic Knowledge representation using non monotonic logic Planning Perception Learning Neural Networks Natural language processing Expert system
  • EL-8 : Soft Computing
    • Fuzzy logic, Neural Networks, Genetic Algorithms
  • EL-9 : Design Concepts and Modeling
    • Introduction to design process, Inception phase, Elaboration phase, Construction phase, Transition phase
  • EL-10 : Software Testing
    • Introduction to software testing and analysis, Specification-based testing techniques, Code-based testing techniques, Unit testing, Integration testing, OO-oriented testing, Model-based testing, Static analysis, Dynamic analysis, Regression testing, Methods of test data generation and validation, Program slicing and its application, Reliability analysis, Formal methods; verification methods; oracles, System and acceptance testing

M.Sc. (Scientific Computing)

Announcements and Updates

What is M.Sc. Scientific Computing? [Watch on ]


ENTRANCE exam 2025 - M.Sc. (Scientific Computing) Click here


Apply for M.Sc. (Scientific Computing).
Last date EXTENDED to : 20th May 2025

Alumni Testimonials