Payyanur College Campus is located at Edat, Payyanur, Kannur Distrcit. It is just 3.5 kilometer from payyanur old bus stand and 2.6 kilometers from new bus stand in NH-17 towards Kannur side.

Nearest railway station is Payyanur which is 6 kms away from the college.

The nearest airport is at Mangalore about 118 km away and calicut airport is 147 km away approximately.

Accredited by NAAC at 'A' Level | Affiliated to Kannur University

Mail: contact@payyanurcollege.ac.in

** DEPARTMENT OF MATHEMATICS**

PEOPLE COURSE RESULT PROJECTS PUBLICATIONS ACTIVITIES UGC-CSIR NET ALUMNI CONTACT

** UGC - CSIR NET EXAMINATIONS
**

**Pattern of the UGC-CSIR NET question paper**

**UGC-CSIR NET: Old Questions and Answers**

** Mathematical Sciences: June 2017 Questions **

** Mathematical Sciences: December 2016 Questions **

** Mathematical Sciences: December 2016 Key **

** Mathematical Sciences: June 2016 Questions **

** Mathematical Sciences: June 2016 Key **

** Mathematical Sciences: December 2015 Questions and Key**

** Mathematical Sciences: June 2014 Questions and Key**

** Mathematical Sciences: December 2013 Questions and Key**

** Mathematical Sciences: June 2013 Questions and Key**

** Mathematical Sciences: December 2012 Questions and Key**

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CSIR-UGC National Eligibility Test for Junior Research Fellowship and Lecturer-ship

**COMMON SYLLABUS FOR PART B AND C**

MATHEMATICAL SCIENCES

**UNIT 1**

Analysis: Elementary set theory, finite, countable and uncountable sets, Real number system as a complete ordered field, Archimedean property, supremum, infimum. Sequences and series, convergence, limsup, liminf. Bolzano Weierstrass theorem, Heine Borel theorem. Continuity, uniform continuity, differentiability, mean value theorem. Sequences and series of functions, uniform convergence. Riemann sums and Riemann integral, Improper Integrals. Monotonic functions, types of discontinuity, functions of bounded variation, Lebesgue measure, Lebesgue integral. Functions of several variables, directional derivative, partial derivative, derivative as a linear transformation, inverse and implicit function theorems. Metric spaces, compactness, connectedness. Normed linear Spaces. Spaces of continuous functions as examples.

Linear Algebra: Vector spaces, subspaces, linear dependence, basis, dimension, algebra of linear transformations. Algebra of matrices, rank and determinant of matrices, linear equations. Eigenvalues and eigenvectors, Cayley-Hamilton theorem. Matrix representation of linear transformations. Change of basis, canonical forms, diagonal forms, triangular forms, Jordan forms. Inner product spaces, orthonormal basis. Quadratic forms, reduction and classification of quadratic forms

**UNIT 2**

**Complex Analysis:**
Algebra of complex numbers, the complex plane, polynomials, power series, transcendental functions such as exponential, trigonometric and hyperbolic
functions. Analytic functions, Cauchy-Riemann equations. Contour integral, Cauchys theorem, Cauchys integral formula, Liouvilles theorem, Maximum
modulus principle, Schwarz lemma, Open mapping theorem.Taylor series, Laurent series, calculus of residues. Conformal mappings, Mobius transformations.

**Algebra:**
Permutations, combinations, pigeon-hole principle, inclusion-exclusion principle, derangements. Fundamental theorem of arithmetic, divisibility in Z,
congruences, Chinese Remainder Theorem, Eulers Ø- function, primitive roots. Groups, subgroups, normal subgroups, quotient groups, homomorphisms, cyclic
groups, permutation groups, Cayley’s theorem, class equations, Sylow theorems. Rings, ideals, prime and maximal ideals, quotient rings, unique
factorization domain, principal ideal domain, Euclidean domain. Polynomial rings and irreducibility criteria. Fields, finite fields, field extensions,
Galois Theory.

**Topology:**
basis, dense sets, subspace and product topology, separation axioms, connectedness and compactness

**UNIT 3**

**Ordinary Differential Equations (ODEs): **
Existence and uniqueness of solutions of initial value problems for first order ordinary differential equations, singular solutions of first order ODEs,
system of first order ODEs. General theory of homogenous and non-homogeneous linear ODEs, variation of parameters, Sturm-Liouville boundary value problem,
Greens function.

**Partial Differential Equations (PDEs): **
Lagrange and Charpit methods for solving first order PDEs, Cauchy problem for first order PDEs. Classification of second order PDEs, General solution of
higher order PDEs with constant coefficients, Method of separation of variables for Laplace, Heat and Wave equations.

**Numerical Analysis : **
Numerical solutions of algebraic equations, Method of iteration and Newton-Raphson method, Rate of convergence, Solution of systems of linear algebraic
equations using Gauss elimination and Gauss-Seidel methods, Finite differences, Lagrange, Hermite and spline interpolation, Numerical differentiation and
integration, Numerical solutions of ODEs using Picard, Euler, modified Euler and Runge-Kutta methods.

**Calculus of Variations: **
Variation of a functional, Euler Lagrange equation, Necessary and sufficient conditions for extrema. Variational methods for boundary value problems in
ordinary and partial differential equations.

**Linear Integral Equations: **
Linear integral equation of the first and second kind of Fredholm and Volterra type, Solutions with separable kernels. Characteristic numbers and
eigenfunctions, resolvent kernel.

**Classical Mechanics: **
Generalized coordinates, Lagranges equations, Hamiltons canonical equations, Hamiltons principle and principle of least action, Two-dimensional motion
of rigid bodies, Eulers dynamical equations for the motion of a rigid body about an axis, theory of small oscillations.

**UNIT 4**

**Descriptive statistics, exploratory data analysis **
Sample space, discrete probability, independent events, Bayes theorem. Random variables and distribution functions (univariate and multivariate);
expectation and moments. Independent random variables, marginal and conditional distributions. Characteristic functions. Probability inequalities
(Tchebyshef, Markov, Jensen). Modes of convergence, weak and strong laws of large numbers, Central Limit theorems (i.i.d. case). Markov chains with finite
and countable state space, classification of states, limiting behaviour of n-step transition probabilities, stationary distribution, Poisson and
birth-and-death processes. Standard discrete and continuous univariate distributions. sampling distributions, standard errors and asymptotic distributions,
distribution of order statistics and range. Methods of estimation, properties of estimators, confidence intervals. Tests of hypotheses: most powerful and
uniformly most powerful tests, likelihood ratio tests. Analysis of discrete data and chi-square test of goodness of fit. Large sample tests. Simple
nonparametric tests for one and two sample problems, rank correlation and test for independence. Elementary Bayesian inference. Gauss-Markov models,
estimability of parameters, best linear unbiased estimators, confidence intervals, tests for linear hypotheses. Analysis of variance and covariance. Fixed,
random and mixed effects models. Simple and multiple linear regression. Elementary regression diagnostics. Logistic regression. Multivariate normal
distribution, Wishart distribution and their properties. Distribution of quadratic forms. Inference for parameters, partial and multiple correlation
coefficients and related tests. Data reduction techniques: Principle component analysis, Discriminant analysis, Cluster analysis, Canonical correlation.
Simple random sampling, stratified sampling and systematic sampling. Probability proportional to size sampling. Ratio and regression methods. Completely
randomized designs, randomized block designs and Latin-square designs. Connectedness and orthogonality of block designs, BIBD. 2K factorial experiments:
confounding and construction. Hazard function and failure rates, censoring and life testing, series and parallel systems. Linear programming problem,
simplex methods, duality. Elementary queuing and inventory models. Steady-state solutions of Markovian queuing models: M/M/1, M/M/1 with limited waiting
space, M/M/C, M/M/C with limited waiting space, M/G/1.

**
All students are expected to answer questions from Unit I. Students in mathematics are expected to answer additional question from Unit II and III.
Students with in statistics are expected to answer additional question from Unit IV
**

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