hrinfomaths@gmail.com | +91 98721-24534 | +91 98786 24534

Connect with Infomaths.

Follow Us

Popular searches

Notifications

Free demo Classes

🎯 Join Our FREE MCA Entrance Demo Session

Enquiry Now Jun 20, 2026

Admission Open 2027-28

🚀 Admissions Open for MCA Entrance 2027-2028! Join Infomaths and start your journey towards NIMCET, CUET PG, PU MCA, MAH MCA CET & more.

Fill the Form Jun 20, 2026

PU Chandigarh PG Admission Notification 2026

PU Chandigarh PG Admission Notification 2026 | Registration, Important Dates & Fee Details

Learn More Jun 20, 2026

Minor Courses

(To be offered to the students of other departments)

Semester Course Code Name of Course Credits
I MAT-M-1 Calculus 6
II MAT-M-2 Algebra and Ordinary Differential Equations 6
III MAT-M-3 Partial Differential Equations and Numerical Analysis 6
IV MAT-M-4 Number Theory and Group Theory 6
V MAT-M-5 Mathematical Modelling 4
VI MAT-M-6 Discrete Mathematics 4
VII MAT-M-7 Special Functions and Integral Transformations 4
VIII MAT-M-8 Advanced Calculus / Linear Algebra 4

The above Minor Courses will be offered only to those students who studied Mathematics in 10+2.

Skill Enhancement Compulsory Courses

(3 Credits)

Semester Course Code Name of Course
I MAT-SEC-1 Discrete Mathematics
II MAT-SEC-2 Working with Mathematical Softwares
IV MAT-SEC-3 Programming in C

Multidisciplinary Courses

(3 Credits)

(To be offered to the students of other departments)

Semester Course Code Name of Course
I, II MAT-IDC Algebra and Geometry
III MAT-IDC-3 Matrices

These IDC courses will be offered only to those students who did not study Mathematics after matriculation.

Students are not permitted to repeat any course they have already completed for credit.

Ability Enhancement Compulsory Courses

A student is required to take two Ability Enhancement Courses (Language) of 2 credits per semester in Semesters I and II from the pool of AECC courses offered by the University.

Value Added Courses

One Value Added Course of 2 credits per semester in Semesters I, II and V shall be chosen from the pool of VAC courses offered by the University.

The department may also offer some of the following Value Added Courses (VACs). These courses are not open to students of B.Sc. (Mathematics) or B.Sc. (Mathematics and Computing). However, the VAC course on Number Theory may be opted by students of B.Sc. (Mathematics and Computing).

Course Code Name of Course
MAT-VAC-1 Logic and Sets
MAT-VAC-2 LaTeX and HTML Theory-I
MAT-VAC-3 Discrete Mathematics
MAT-VAC-4 Matrices and Their Applications
MAT-VAC-5 Graph Theory
MAT-VAC-6 Probability and Statistics
MAT-VAC-7 Number Theory

Students are not permitted to repeat any course they have already completed for credit.

MAT-DSC-6: Numerical Analysis (Theory)

Credits: 4

Contact Hours: 60 (4 Hours per Week including Tutorials)

Maximum Marks: 100 (Including Internal Assessment = 20)

Time Allowed for Examination: 3 Hours

Instructions for the Candidates and Paper Setters

Candidates will be required to attempt five questions out of nine questions carrying equal marks.

Question No. 1, covering the entire syllabus, will be compulsory.

There will be two questions from each unit, and students will be required to attempt one question from each unit.

Learning Outcome

The objective of this course is to make students familiar with numerical methods used to obtain approximate numerical solutions of mathematical problems that may not have exact closed-form solutions.

Students will also learn to effectively use computational techniques and software tools in numerical computations.

Unit-I

Algorithms, Convergence and Error Analysis

  • Algorithms
  • Convergence
  • Error Analysis
  • Relative Error
  • Truncation Error
  • Round-off Error
  • Order of Approximation
  • Order of Convergence

Methods for Solving Algebraic and Transcendental Equations

  • Bisection Method
  • False Position Method
  • Fixed Point Iteration Method
  • Newton’s Method
  • Secant Method

Unit-II

Techniques to Solve Linear Systems

  • Gaussian Elimination Method
  • Gauss-Jordan Method
  • Gauss-Jacobi Method
  • Gauss-Seidel Method
  • LU Decomposition Method
  • Successive Over-Relaxation (SOR) Iteration Method
  • Convergence of Iterative Methods

Unit-III

Interpolation

  • Errors in Polynomial Interpolation
  • Finite Difference Operators
  • Newton’s Gregory Forward Interpolation Formula
  • Newton’s Gregory Backward Interpolation Formula
  • Central Difference Interpolation Formula
    • Gauss Formula
    • Stirling’s Formula
    • Bessel’s Formula
    • Everett’s Formula
  • Lagrange Interpolation Formula
  • Newton Divided Difference Interpolation Formula

Unit-IV

Numerical Differentiation and Integration

  • First and higher-order approximation for first derivative
  • Approximation for second derivative
  • Richardson Extrapolation Method
  • Numerical integration by closed Newton-Cotes formulae
  • Trapezoidal Rule
  • Simpson’s Rule and its error analysis

Numerical Solution of Ordinary Differential Equations

  • Euler’s Method
  • Second Order Runge-Kutta Methods
  • Modified Euler’s Method
  • RK2 Method

MAT-DSC-6: Numerical Analysis (Practicals)

(Using any Programming Language or Mathematical Software)

Credits: 2

Contact Hours: 4 Hours per Week (2 Practicals per Week)

Maximum Marks: 50 (Including Internal Assessment = 10)

Time Allowed for Examination: 3 Hours

Learning Outcome

The objective of this practical course is to make students familiar with numerical methods used for obtaining approximate numerical solutions to mathematical problems that may not have exact closed-form solutions.

Students will also gain hands-on experience in implementing numerical techniques using programming languages or mathematical software tools.

Practical Sessions

Two practical sessions of two hours each will be conducted every week in accordance with the topics covered in the theory syllabus.

Essential Textbook

(A) R. K. S. Iyengar and R. K. Jain, Numerical Methods, New Age International, 2009.

Further Readings

  1. R. L. Burden and J. D. Faires, Numerical Analysis, 9th Edition, Brooks/Cole, Cengage Learning, 2011.
  2. C. F. Gerald and P. O. Wheatley, Applied Numerical Analysis, Pearson, 2010.
  3. R. S. Gupta, Elements of Numerical Analysis, Cambridge University Press, 2015.
  4. F. B. Hildebrand, Introduction to Numerical Analysis, Courier Corporation, 1987.
  5. Brian Bradie, A Friendly Introduction to Numerical Analysis, Pearson, 2007.
  6. Uri M. Ascher and Chen Greif, A First Course in Numerical Methods, 7th Edition, PHI, 2013.
  7. John H. Mathews and Kurtis D. Fink, Numerical Methods using Matlab, PHI, 2012.
  8. S. S. Shastry, Introductory Methods of Numerical Analysis, PHI, 2012.