M. Sc. Statistical Sciences
Agricultural Statistics
 Probability Theory
 Statistical Methods
 Statistical Inference
 Design of Experiments
 Sampling Techniques
 Statistical Genetics
 Multivariate Analysis
 Regression Analysis
 Statistical Computing
 Seminar
 Research
 MathematicsI
 Actuarial Statistics
 Bioinformatics
 Econometrics
 MathematicsII
 Statistical Quality Control
 Optimization Techniques
 Time Series Analysis
 Demography
 Statistical Methods for Life Sciences
 Statistical Ecology
 Mathematics for Applied Sciences
 Statistical Methods for Applied Sciences
 Experimental Designs
 Basic Sampling Techniques
 Applied Regression Analysis
 Data Analysis Using Statistical Packages
Computer Application
 Mathematics for Applied Sciences
 Statistical Computing
 Mathematical Foundations in Computer Science
 Object Oriented Programming
 Design and Analysis of Algorithms
 Data Structures
 System Software and Programming
 Internet Technologies
 Database Management Systems
 Software Engineering
 Master’s Seminar
 Master’s Research
 Information Security
 Web Technologies and Applications
 Computer Networks
 Bioinformatics Computing
 Soft Computing Techniques
 Operating System
 Compiler Construction
 Data Warehousing and Data Mining
Agricultural Statistics

Mathematics for Applied Sciences
Content: Set theoryset operations, finite and infinite sets, operations of set, function. Vectors and vector spaces, Matrices notations and operations, laws of matrix algebra; transpose and inverse of matrix, Eigen values and Eigen vectors. Determinants  evaluation and properties of determinants, Solutions of Linear Equations. Variables and functions, limits and continuity of specific functions. Differentiation: theorems of differentiation, differentiation of logarithmic, trigonometric, exponential and inverse functions, Differentiation of function of a function, derivatives of higher order, partial derivatives. Application of derivatives, determination of points of inflexion, maxima and minima. Integration, methods of integration, reduction formulae, definite and indefinite integral, Applications of integration in Agriculture, Differential Equations.

Statistical Methods for Applied Sciences
Content: Boxplot, Descriptive statistics, Exploratory data analysis, Theory of probability, Random variable and mathematical expectation. Discrete and continuous probability distributions, Binomial, Poisson, Negative Binomial, Normal distribution, Beta and Gamma distributions and their applications. Concept of sampling distribution: chisquare, t and F distributions. Tests of significance based on Normal, chisquare, t and F distributions. Introduction to theory of estimation and confidenceintervals, Simple and multiple correlation coefficient, partial correlation, rank correlation, Simple and multiple linear regression model, test of significance of correlation coefficient and regression coefficients, Coefficient of determination, Fitting of quadratic models. Nonparametric tests – sign, Wilcoxon, MannWhitney Utest, Run test for the randomness of a sequence. Median test. Introduction to ANOVA: One way and Two Way, Introduction to Sampling Techniques, Introduction to Multivariate Analysis, Transformation of Data.
Practical: Exploratory data analysis, fitting of distributions ~ Binomial, Poisson, Negative Binomial, Normal. Large sample tests, testing of hypothesis based on exact sampling distributions ~ chi square, t and F. Confidence interval estimation and Correlation and regression analysis, fitting of Linear and Quadratic Model. Nonparametric tests. ANOVA: One way, Two Way, SRS.

Experimental Designs
Content: Need for designing of experiments, characteristics of a good design. Basic principles of designs randomization, replication and local control. Uniformity trials, size and shape of plots and blocks, Analysis of variance, Completely randomized design, randomized block design and Latin square design. Factorial experiments, (symmetrical as well as asymmetrical). orthogonality and partitioning of degrees of freedom. Concept of confounding. Split plot and strip plot designs, analysis of covariance and missing plot techniques in randomized block and Latin square designs; Transformations, Balanced Incomplete Block Design, resolvable designs and their applications, Lattice design, alpha design  concepts, randomization procedure, analysis and interpretation of results. Response surfaces. Combined analysis.
Practical: Uniformity trial data analysis, formation of plots and blocks, Fairfield Smith Law, Analysis of data obtained from CRD, RBD, LSD, Analysis of factorial experiments; Analysis with missing data; Split plot and strip plot designs.

Basic Sampling Techniques
Content: Concept of sampling, sample survey vs complete enumeration, planning of sample survey, sampling from a finite population. Simple random sampling with and without replacement, sampling for proportion, determination of sample size, inverse sampling, Stratified sampling. Cluster sampling, Multistage sampling, systematic sampling; Introduction to PPS sampling, Use of auxiliary information at estimation, Ratio product and regression estimators. Double Sampling, sampling and nonsampling errors.
Practical: Random sampling ~ use of random number tables, concepts of unbiasedness, variance, etc.; Simple random sampling, determination of sample size, inverse sampling, stratified sampling, cluster sampling and systematic sampling; Estimation using ratio and regression estimators; Estimation using multistage design, double sampling.

Applied Regression Analysis
Content: Introduction to correlation analysis and its measures, Correlation from grouped data, correlation, Rank correlation, Testing of population correlation coefficients; Multiple and partial correlation coefficients and their testing. Problem of correlated errors; Auto correlation; Heteroscedastic models, Durbin Watson Statistics; Removal of auto correlation by transformation; Analysis of collinear data; Detection and correction of multi collinearity, Regression analysis; Method of least squares for curve fitting; Testing of regression coefficients; Multiple and partial regressions. Diagnostic of multiple regression equation; Concept of weighted least squares; regression equation on grouped data; Various methods of selecting the best regression equation. Concept of nonlinear regression and fitting of quadratic, exponential and power curves; Economic and optimal dose, Orthogonal polynomial.
Practical: Correlation coefficient, various types of correlation coefficients, partial and multiple, testing of hypotheses; Multiple linear regression analysis, partial regression coefficients, testing of hypotheses, residuals and their applications in outlier detection; Handling of correlated errors, multi collinearity; Fitting of quadratic, exponential and power curves, fitting oforthogonal polynomials.

Data Analysis Using Statistical Packages
Content: Introduction to various statistical packages: Excel, R, SAS, SPSS. Data Preparation; Descriptive statistics; Graphical representation of data, Exploratory data analysis. Unit II Test for normality; Testing of hypothesis using chisquare, t and F statistics and Ztest. Data preparation for ANOVA and ANCOVA, Factorial Experiments, contrast analysis, multiple comparisons, Analyzing crossed and nested classified designs. Analysis of mixed models; Estimation of variance components; Correlation and regression analysis, Probit, Logit and Tobit Models. Discriminant function; Factor analysis; Principal component analysis; Analysis of time series data, Fitting of nonlinear models; Neural networks.
Practical: Use of software packages for summarization and tabulation of data, obtaining descriptive statistics, graphical representation of data; Testing the hypothesis for one sample ttest, two sample ttest, paired ttest, test for large samples  Chisquares test, F test, oneway analysis of variance; Designs for Factorial Experiments, fixed effect models, random effect models, mixed effect models, estimation of variance components; Linear regression, Multiple regression, Regression plots; Discriminant analysis  fitting of discriminant functions, identification of important variables; Factor analysis. Principal component analysis  obtaining principal component.

MathematicsI
Content: Calculus: Limit and continuity, differentiation of functions, successive differentiation, partial differentiation, mean value theorems, Taylor and Maclaurin’s series. Application of derivatives, L’hospital’s rule. Real Analysis: Convergence and divergence of infinite series, use of comparison tests D’Alembert’s Ratio  test, Cauchy’s nth root test, Raabe’s test, Kummer’s test, Gauss test. Absolute and conditional convergence. Riemann integration, concept of Lebesgue integration, power series, Fourier, Laplace and Laplace Steiltjes’ transformation, multiple integrals.Integration of rational, irrational and trigonometric functions. Application of integration. Differential equation: Differential equations of first order, linear differential equations of higher order with constant coefficient. Numerical Analysis: Simple interpolation, Divided differences, Numerical differentiation and integration.

Probability Theory
Content: Basic concepts of probability. Elements of measure theory: class of sets, field, sigma field, minimal sigma field, Borel sigma field in R, measure probability measure. Axiomatic approach to probability. Properties of probability based on axiomatic definition. Addition and multiplication theorems. Conditional probability and independence of events. Bayes theorem. Random variables: definition of random variable, discrete and continuous, functions of random variables. Probability mass function and Probability density function, Distribution function and its properties. Notion of bivariate random variables, bivariate distribution function and its properties. Joint, marginal and conditional distributions. Independence of random variables. Transformation of random variables (two dimensional caseonly). Mathematical expectation: Mathematical expectation of functions of a random variable. Raw and central moments and their relation, covariance, skewness and kurtosis. Addition and multiplication theorems of expectation. Definition of moment generating function, cumulating generating function, probability generating function and statements of their properties. Conditional expectation and conditional variance. Characteristic function and its properties. Inversion and uniqueness theorems. Chebyshev, Markov, CauchySchwartz, Sequence of random variables and modes of convergence (convergence in distribution in probability, almost surely, and quadratic mean) and their interrelations. Laws of large numbers: WLLN, Bernoulli and Kintchin’s WLLN. Kolmogorov inequality, Kolmogorov‘s SLLNs.Central Limit theorems: Demoviere Laplace CLT, Lindberg – Levy CLT and simple applications.

Statistical Methods
Content: Descriptive statistics: probability distributions: Discrete probability distributions ~ Bernoulli, Binomial, Poisson, Negativebinomial, Geometric and Hyper Geometric, uniform, multinomial ~ Properties of these distributions and real life examples. Continuous probability distributions ~ rectangular, exponential, Cauchy, normal, gamma, beta of two kinds, Weibull, lognormal, logistic, Pareto. Properties of these distributions. Probability distributions of functions of random variables. Concepts of compound, truncated and mixture distributions (definitions and examples). Sampling distributions of sample mean and sample variance from Normal population, central and non–central chiSquare, t and F distributions, their properties and inter relationships. Concepts of random vectors, moments and their distributions. Bivariate Normal distribution  marginal and conditional distributions. Distribution of quadratic forms. Cochran theorem. Correlation, rank correlation, correlation ratio and intraclass correlation. Regression analysis, partial and multiple correlation andregression. Sampling distribution of correlation coefficient, regression coefficient. Categorical data analysis, Association between attributes. Variance StabilizingTransformations. Order statistics, distribution of rth order statistics, joint distribution of several order statistics and their functions, marginal distributions of order statistics.
Practical: Fitting of discrete distributions and test for goodness of fit; Fitting of continuous distributions and test for goodness of fit; Fitting of truncated distribution; Computation of simple, multiple and partial correlation coefficient, correlation ratio and intraclass correlation; Regression coefficients and regression equations; Fitting of Pearsonian curves; Analysis of association between attributes, categorical data and loglinear models.

Actuarial Statistics
Content: Insurance and utility theory, models for individual claims and their sums, survival function, curtate future lifetime, force of mortality. Life table and its relation with survival function, examples, assumptions for fractional ages, some analytical laws of mortality, select and ultimate tables. Multiple life functions, joint life and last survivor status, insurance and annuity benefits through multiple life functions evaluation for special mortality laws. Multiple decrement models, deterministic and random survivorship groups, associated single decrement tables, central rates of multiple decrement, net single premiums and their numerical evaluations. Distribution of aggregate claims, compound Poisson distribution and its applications. Principles of compound interest: Nominal and effective rates of interest and discount, force of interest and discount, compound interest, accumulation factor, continuous compounding. Insurance payable at the moment of death and at the end of the year of deathlevel benefit insurance, endowment insurance, deferred insurance and varying benefit insurance, recursions, commutation functions. Life annuities: Single payment, continuous life annuities, discrete life annuities, life annuities with monthly payments, commutation functions, varying annuities, recursions, complete annuitiesimmediate and apportionable annuitiesdue. Net premiums: Continuous and discrete premiums, true monthly payment premiums, apportionable premiums, commutation functions, accumulation type benefits. Payment premiums, apportionable premiums, commutation functions, accumulation type benefits. Net premium reserves: Continuous and discrete net premium reserve, reserves on a semicontinuous basis, reserves based on true monthly premiums, reserves on an apportionable or discounted continuous basis, reserves at fractional durations, allocations of loss to policy years, recursive formulas and differential equations for reserves, commutation functions. Some practical considerations: Premiums that include expensesgeneral expenses types of expenses, per policy expenses. Claim amount distributions, approximating the individual model, stoploss insurance.

Bioinformatics
Content: Basic Biology; Cell, genes, gene structures, gene expression and regulation, Molecular tools, nucleotides, nucleic acids, markers, proteins and enzymes, bioenergetics, single nucleotide polymorphism, expressed sequence tag. Structural and functional genomics: Organization and structure of genomes, genome mapping, assembling of physical maps, strategies and techniques for genome sequencing and analysis. Computing techniques: OS and Programming Languages – Linux, perl, bioperl,python, biopython,cgi, MySQL, phpMyAdmin; Coding for browsing biological databases on web, parsing & annotation of genomic sequences; Database designing; Computer networks – Internet, World wide web, Web browsers– EMBnet, NCBI; Databases on public domain pertaining to Nucleic acid sequences, protein sequences, SNPs, etc.; Searching sequence databases, Structural databases. Statistical Techniques: MANOVA, Cluster analysis, Discriminant analysis, Principal component analysis, Principal coordinate analysis, Multidimensional scaling; Multiple regression analysis; Likelihood approach in estimation and testing; Resampling techniques – Bootstrapping and Jackknifing; Hidden Markov Models; Bayesian estimation and Gibbs sampling; Tools for Bioinformatics: DNA Sequence Analysis – Features of DNA sequence analysis, Approaches to EST analysis; Pairwise alignment techniques: Comparing two sequences, PAM and BLOSUM, Global alignment (The Needleman and Wunsch algorithm), Local Alignment (The SmithWaterman algorithm), Dynamic programming, Pairwise database searching; Sequence analysis– BLAST and other related tools, Multiple alignment and database search using motif models, ClustalW, Phylogeny; Databases on SNPs; EM algorithm and other methods to discover common motifs in biosequences; Gene prediction based on Neural Networks, Genetic algorithms, Computational analysis of protein sequence, structure and function; Design and Analysis of microarray/ RNAseqexperiments.

Econometrics
Content: Representation of Economic phenomenon, relationship among economic variables, linear and nonlinear economic models, single equation general linear regression model, basic assumptions, Ordinary least squares method of estimation for simple and multiple regression models; summary statistics correlation matrix, coefficient of multiple determination, standard errors of estimated parameters, tests of significance and confidence interval estimation. BLUE properties of Least Squares estimates. Chow test, test of improvement of fit through additional regressors. Maximum likelihood estimation. Heteroscedasticity, Autocorrelation, Durbin Watson test, Multicollinearity. Stochastic regressors, Errors in variables, Use of instrumental variables in regression analysis. Dummy Variables. Distributed Lag models: Koyck’s Geometric Lag scheme, Adaptive Expectation and Partial Adjustment Mode, Rational Expectation Models and test for rationality. Simultaneous equation model: Basic rationale, Consequences of simultaneous relations, Identification problem, Conditions of Identification, Indirect Least Squares, Twostage least squares, Kclass estimators, Limited Information and Full Information Maximum Likelihood Methods, three stage least squares, Generalized least squares, Recursive models, SURE Models. Mixed Estimation Methods, use of instrumental variables, pooling of crosssection and time series data, Principal Component Methods. Problem and Construction of index numbers and their tests; fixed and chain based index numbers; Construction of cost of living index number. Unit V Demand analysis – Demand and Supply Curves; Determination of demand curves from market data. Engel’s Law and the Engel’s Curves, Income distribution and method of its estimation, Pareto’s Curve, Income inequality measures.

MathematicsII
Content: Linear Algebra: Group, ring, field and vector spaces, Subspaces, basis, Gram Schmidt’s orthogonalization, Galois field  Fermat’s theorem and primitive elements. Linear transformations. Graph theory: Concepts and applications. Matrix Algebra: Basic terminology, linear independence and dependence of vectors. Row and column spaces, Echelon form. Determinants, Trace of matrices rank and inverse of matrices. Special matrices – idempotent, symmetric, orthogonal. Eigen values and eigen vectors, Spectral decomposition of matrices. Unitary, Similar, Hadamard, Circulant, Helmert’s matrices. Kronecker and Hadamard product of matrices, Kronecker sum of matrices. Submatrices and partitioned matrices, Permutation matrices, full rank factorization, Grammian root of a symmetric matrix. Solutions of linear equations, Equations having many solutions. Generalized inverses, MoorePenrose inverse, Applications of ginverse. Inverse and Generalized inverse of partitioned matrices, Differentiation and integration of vectors and matrices, Quadratic forms.

Statistical Inference
Content: Concepts of point estimation: unbiasedness, consistency, efficiency and sufficiency. Statement of Neyman’s Factorization theorem with applications. MVUE, RaoBlackwell theorem, completeness, Lehmann Scheffe theorem. Fisher information, CramerRao lower bound and its applications. Moments, minimum chisquare, least square and maximum likelihood methods of estimation and theirproperties.Interval estimationConfidence level, shortest length CI. CI for the parameters of Normal, Exponential, Binomial and Poisson distributions. Fundamentals of hypothesis testingstatistical hypothesis, statistical test, critical region, types of errors, test function, randomized and non randomized tests, level of significance, power function, most powerful tests: NeymanPearson fundamental lemma, MLR families and UMP tests for one parameter exponential families. Concepts of consistency, unbiasedness and invariance of tests. Likelihood Ratio tests, asymptotic properties of LR tests with applications (including homogeneity of means and variances).Relation between confidence interval estimation and testing of hypothesis. Sequential Probability ratio test, Properties of SPRT.Termination property of SPRT, SPRT for Binomial, Poisson, Normal and Exponential distributions. Concepts of loss, risk and decision functions, admissible and optimal decision functions, estimation and testing viewed as decision problems, conjugate families, Bayes and Minimax decision functions with applications to estimation with quadratic loss. Nonparametric tests: Sign test, Wilcoxon signed rank test, Runs test for randomness, Kolmogorov – Smirnov test for goodness of fit, Median test and WilcoxonMannWhitney Utest. Chisquare test for goodness of fit and test for independence of attributes. Spearman’s rank correlation and Kendall’s Tau tests for independence.
Practical: Methods of estimation  Maximum Likelihood, Minimum c2 and Moments; Confidence Interval Estimation; MP and UMP tests; Large Sample tests; Nonparametric tests, Sequential Probability Ratio Test; Decision functions.

Design of Experiments
Content: Elements of linear estimation, Gauss Markoff Theorem, relationship between BLUEs and linear zerofunctions. Aitken’s transformation, test of hypothesis, Analysis of Variance, Partitioning of degrees of freedom. Orthogonality, contrasts, mutually orthogonal contrasts, analysis of covariance; Basic principles of design of experiments, uniformity trials, size and shape of plots andblocks, Randomization procedure. Basic designs  completely randomized design, randomized complete block design and Latin square design; Construction of orthogonal Latin squares, mutually orthogonal Latin squares (MOLS), Youden square designs, Graeco Latin squares. Balanced Incomplete Block (BIB) designs – general properties and analysis without and with recovery of intra block information, construction of BIB designs. Partially balanced incomplete block designs with two associate classes  properties, analysis and construction, Lattice designs, alpha designs, cyclic designs, augmented designs. Factorial experiments, confounding in symmetrical factorial experiments (2nand 3nseries), partial and total confounding, asymmetrical factorials. Crossover designs. Missing plot technique; Split plot and Strip plot design; Groups of experiments.Sampling in field experiments.
Practical: Determination of size and shape of plots and blocks from uniformity trials data; Analysis of data generated from completely randomized design, randomized complete block design; Latin square design, Youden square design; Analysis of data generated from a BIB design, lattice design, PBIB designs; 2n, 3n factorial experiments without and with confounding; Split and strip plot designs, repeated measurement design; Missing plot techniques, Analysis of covariance; Analysis of Groups of experiments, Analysis of clinical trial experiments.

Sampling Techniques
Content: Sample survey vs complete enumeration, probability sampling, sample space, sampling design, sampling strategy; Determination of sample size; Confidenceinterval; Simple random sampling, Estimation of population proportion, Stratified random sampling, Proportional allocation and optimal allocation, Inverse sampling. Ratio, Product and regression methods of estimation, Cluster sampling, Systematic sampling, Multistage sampling with equal probability, Separate and combined ratio estimator, Double sampling, Successive sampling –two occasions. Unbiased ratio type estimators. Nonsampling errors – sources and classification, Nonresponse in surveys, Randomized response techniques, Response errors/ Measurement error – interpenetrating subsampling. PPS Sampling with and without replacement, Cumulative method and Lahiri’s method of selection, HorvitzThompson estimator, Ordered and unordered estimators, Sampling strategies due to MidzunoSen and RaoHartleyCochran. Inclusion probability proportional to size sampling.
Practical: Determination of sample size and selection of sample; Simple random sampling, Inverse sampling, Stratified random sampling, Cluster sampling, systematic sampling; Ratio and regression methods of estimation; Double sampling, multistage sampling, Imputation methods; Randomized response techniques; Sampling with varying probabilities.

Statistical Genetics
Content: Physical basis of inheritance. Analysis of segregation, detection and estimation of linkage for qualitative characters. Amount of information about linkage, combined estimation, disturbed segregation. Gene and genotypic frequencies, Random mating and Hardy Weinberg law, Application and extension of the equilibrium law, Fisher’s fundamental theorem of natural selection. Disequilibrium due to linkage for two pairs of genes, sexlinked genes, Theory of path coefficients. Concepts of inbreeding, Regular system of inbreeding. Forces affecting gene frequency  selection, mutation and migration, equilibrium between forces in large populations, Random genetic drift, Effect of finite populationsize. Polygenic system for quantitative characters, concepts of breeding value and dominance deviation. Genetic variance and its partitioning, Effect of inbreeding on quantitative characters, Multipleallelism in continuous variation, Sexlinked genes, Maternal effects  estimation of their contribution. Correlations between relatives, Heritability, Repeatability and Genetic correlation. Response due to selection, Selection index and its applications in plants and animals’ improvement programmes, Correlated response to selection. Restricted selection index. Variance component approach and linear regression approach for the analysis of GE interactions. Measurement of stability and adaptability for genotypes. Concepts of general and specific combining ability. Diallel and partial diallel crosses  construction and analysis.
Practical: Test for the single factor segregation ratios, homogeneity of the families with regard to single factor segregation; Detection and estimation of linkage parameter by different procedures; Estimation of genotypic and gene frequency from a given data. HardyWeinberg law; Estimation of changes in gene frequency due to systematic forces, inbreeding coefficient, genetic components of variation, heritability and repeatability coefficient, genetic correlation coefficient; Examination of effect of linkage, epistasis and inbreeding on mean and variance of metric traits; Mating designs; Construction of selection index including phenotypic index, restricted selection index. Correlated response to selection.

Statistical Quality Control
Content: Introduction to Statistical Quality Control; Control Charts for Variables – Mean, Standard deviation and Range charts; Statistical basis; Rational subgroups. Control charts for attributes ‘np’, ‘p’ and ‘c’ charts. Fundamental concepts of acceptance, sampling plans, single, double and sequential sampling plans for attributes inspection. Sampling inspection tables for selection of single and double sampling plans.

Optimization Techniques
Content: Classification of optimization problems, Classical optimization techniques: single variable optimization, multivariable optimization techniques with no constraints, multivariable optimization techniques with equality constraints, multivariable optimization techniques with inequality constraints. Linear programming: simplex method, duality, sensitivity analysis, Karmarkar’s method, transportation problem. Nonlinear programming Unconstrained optimization techniques: direct search methods such as random search, grid search, Hooke and Jeeves’ method, Powel’s method. Descent methods such as gradient method, steepest descent method, conjugate gradient method, Newton’s method, Marquardt method. Quadratic programming, integer linear programming, integer nonlinear programming, geometric programming, dynamic programming, stochastic programming, multiobjective optimization, optimal control theory, genetic algorithms, simulated annealing, neural network based optimization.
Practical: Problems based on classical optimization techniques, optimization techniques with constraints, minimization problems using numerical methods. Linear programming (LP) problems through graphical method, simplex method, simplex twophase method, primal and dual method. Sensitivity analysis for LP problem, LP problem using Karmarkar’s method. Problems based on Quadratic programming, integer programming, dynamic programming, stochastic programming. Problems based on Pontryagin’s maximum principle. Problems based on multiobjective optimization.

Multivariate Analysis
Content: Concept of random vector, its expectation and VarianceCovariance matrix. Marginal and joint distributions. Conditional distributions and Independence of random vectors. Multinomial distribution. Multivariate Normal distribution, marginal and conditional distributions. Sample mean vector and its distribution. Maximum likelihood estimates of mean vector and dispersion matrix. Tests of hypothesis about mean vector. Wishart distribution and its simple properties. Hotelling’s T2 and Mahalanobis D2 statistics. Null distribution of Hotelling’s T2. Rao’s U statistics and its distribution. Wilks’ O criterion and its properties. Concepts of discriminant analysis, computation of linear discriminant function, classification between k (t2) multivariate normal populations based on LDF and Mahalanobis D2. Principal Component Analysis, factor analysis. Canonical variables and canonical correlations. Cluster analysis: similarities and dissimilarities of qualitative and quantitative characteristics, Hierarchical clustering. Single, Complete and Average linkage methods. Kmeans cluster analysis. Path analysis and computation of path coefficients, introduction to multidimensional scaling, some theoretical results, similarities, metric and nonmetric scaling methods.
Practical: Maximum likelihood estimates of meanvector and dispersion matrix; Testing of hypothesis on mean vectors of multivariate normal populations; Cluster analysis, Discriminant function, Canonical correlation, Principal component analysis, Factor analysis; Multivariate analysis of variance and covariance, multidimensional scaling.

Regression Analysis
Content: Simple and Multiple linear regressions: Least squares fit, Properties and examples. Polynomial regression: Use of orthogonal polynomials. Assumptions of regression; diagnostics and transformations; residual analysis ~ Studentized residuals, applications of residuals in detecting outliers, identification of influential observations. Lack of fit, Pure error. Test of normality, test of linearity,Testing homoscedasticity and normality of errors, DurbinWatson test. Test of goodness of fit for the model evaluation and validation.Concept of multicollinearity Weighted least squares method: Properties, and examples. BoxCox family of transformations. Use of dummy variables, Over fitting and under fitting of model, Selection of variables: Forward selection, Backward elimination. Stepwise and Stagewise regressions. Introduction to nonlinear models, nonlinear estimation: Least squares for nonlinear models.
Practical: Multiple regression fitting with three and four independent variables; Estimation of residuals, their applications in outlier detection, distribution of residuals; Test of homoscedasticity, and normality, BoxCox transformation; Restricted estimation of parameters in the model, hypothesis testing, Step wise regression analysis; Least median of squares norm, Orthogonal polynomialfitting.

Statistical Computing
Content: Introduction to statistical packages and computing: data types and structures, Use of Software packages like, SAS, SPSS or “R: The R Project for Statistical Computing”. Data analysis principles and practice, Summarization and tabulation of data, Exploratory data analysis; Graphical representation of data.Statistical Distributions: Fitting and testing the goodness of fit of discrete and continuous probability distributions; ANOVA, regression and categorical data methods; model formulation, fitting, diagnostics and validation; Matrix computations in linear models. Analysis of discrete data. Multiple comparisons, Contrast analysis. Numerical linear algebra, numerical optimization, graphical techniques, numerical approximations, Time Series Analysis. Analysis of mixed models; Estimation of variance components, Analysis of Covariance, Fitting of nonlinear model, Discriminant function; Principal component analysis. techniques in the analysis of survival data and longitudinal studies, Approaches to handling missing data, and metaanalysis.
Practical: Data management, Graphical representation of data, Descriptive statistics; General linear models ~ fitting and analysis of residuals, outlier detection; Fitting and testing the goodness of fit of probability distributions; Testing the hypothesis for one sample ttest, two sample ttest, paired ttest, test for large samples  Chisquares test, F test, One way analysis of variance, contrast and its testing, pairwise comparisons; Mixed effect models, estimation of variance components; Categorical data analysis, dissimilarity measures, similarity measures; Analysis of discrete data, analysis of binary data; Numerical algorithms; Spatial modeling, cohort studies; Clinical trials, analysis of survival data; Handling missing data.Analysis of time series data  fitting of ARIMA models.

Time Series Analysis
Content: Components of a timeseries. Autocorrelation and Partial autocorrelation functions, Correlogram and periodogram analysis. Linear stationary models: Autoregressive, moving average and Mixed processes. Linear nonstationary models: Autoregressive integrated moving average processes. Forecasting: Minimum mean square forecasts and their properties, Calculating and updating forecasts. Model identification: Objectives, Techniques, and Initial estimates. Model estimation: Likelihood function, Sum of squares function, Least squares estimates. Seasonal models. Intervention analysis models and Outlier detection.
Practical: Time series analysis, autocorrelations, correlogram and periodogram; Linear stationary model; Linear nonstationary model; Model identification and model estimation; Intervention analysis and outlier detection.

Demography II
Content: Introduction to vital statistics, crude and standard mortality and morbidity rates, Estimation of mortality, Measures of fertility and mortality, period and cohort measures. Life tables and their applications, methods of construction of abridged life tables, IncrementDecrement Life Tables. Stationary and stable populations, Migration and immigration. Application of stable population theory to estimate vital rates, migration and its estimation. Demographic relations in Nonstable populations. Measurement of population growth, Lotka’s model (deterministic) and intrinsic rate of growth, Measures of mortality and morbidityPeriod. Principle of biological assays, parallel line and slope ratio assays, choice of doses and efficiency in assays quantal responses, probit and logit transformations, epidemiological models.

Statistical Methods for Life Sciences
Content: Proportions and counts, contingency tables, logistic regression models, Poisson regression and loglinear models, models for polytomous data and generalized linear models. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications. Analysis of survival time data using parametric and non parametric models, hypothesis testing, and methods for analyzing censored (partially observed) data with covariates. Topics include marginal estimation of a survival function, estimation of a generalized multivariate linear regression model (allowing missing covariates and/or outcomes). Proportional Hazard model: Methods of estimation, estimation of survival functions, timedependent covariates, estimation of a multiplicative intensity model (such as Cox proportional hazards model) and estimation of causal parameters assuming marginal structural models. General theory for developing locally efficient estimators of the parameters of interest in censored data models. Rank tests with censored data. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications. Newton, scoring, and EM algorithms for maximization; smoothing methods; bootstrapping; trees and neural networks; clustering; isotonic regression; Markov chain Monte Carlomethods.

Statistical Ecology
Content: Ecological data, Ecological sampling; Spatial pattern analysis: Distribution methods, Quadrantvariance methods, Distancemethods. Speciesabundance relations: Distribution models, Diversity indices; Species affinity: Nicheoverlap indices, interspecific association, interspecificcovariation. Community classification: Resemblance functions, Association analysis, Cluster analysis; Community Ordination: Polar Ordination, Principal Component Analysis, Correspondence analysis, Nonlinear ordination. Community interpretation: Classification Interpretation and Ordination Interpretation.
Computer Application

Computer Fundamentals and Programming
Content: Functional units of computer, I/O devices, primary and secondary memories. Number systems: decimal, octal, binary and hexadecimal; Representation of integers, fixed and floating point numbers, Operator precedence, character representation; ASCII, Unicode. Programming Fundamentals with C  Algorithm, techniques of problem solving, flowcharting, stepwise refinement; Constants and variables; Data types: integer, character, real, data types; Arithmetic expressions, assignment statements, logical expressions. Control flow. Arrays and structures. Pointers, dynamic memory allocations. Program Structures – functions, subroutines Unit V I/O operations, Program correctness; Debugging and testing of programs.
Practical: Conversion of different number types; Creation of flow chart, conversion of algorithm/flowchart to program; Mathematical operators, operator precedence; Sequence, control and iteration; Arrays and string processing; Matrix operations, Sorting, Pointers and File processing – Reading and writing text files.

Computer Organization and Architecture
Content: Number systems; Boolean algebra  minimization of Boolean function using KarnaughMap. Logic Gates, Combinational circuits – multiplexer, demultiplexer, encoder, decoder; Sequential circuits: Flipflops, Half and Full adder, Shift register, Counters. Organization of CPU, Control Unit Instruction and Execution cycle in CPU, Register Organization, The Instruction Cycle, Instruction Pipelining. Memory organization  Internal memory: Semiconductor Main Memory (RAM, ROM, EPROM), Cache Memory, Advanced DRAM Organization; External Memory  Magnetic Disks, RAID, Optical Memory, Magnetic Tape. Basic structure of computer hardware and system software  Addressing methods and machine programme sequencing; Inputoutput organizations  accessing I/O devices  direct memory access (DMA) – interrupts. Introduction to microprocessors – CISC and RISC Architecture, Study of functional units of microprocessors.

Introduction to Networking and Internet Applications
Content: Networking fundamentals, types of networking, network topology; Introduction to File Transfer Protocol (FTP), Telnet, Simple Mail Transfer Protocol (SMTP), Internet Protocol v4 & v6. Network infrastructure and Securityswitches, routers, firewall, intranet, internet, Virtual Private Network. World Wide Web (www), working with Internet; Web pages, web sites, web servers; Web Applications. Hyper Text Markup Language (HTML), DHTML, web based application development. Static websites, dynamic websites. Client Side processing – scripting languages, Jquery. Server Side processing ASP.NET/JSP
Practical: Network and mail configuration; Using Network Services; Browsing of Internet; Creation of web pages; Creation of websites using HTML and scripting languages.

Information Technology in Agriculture
Content: Introduction to Computers, Anatomy of computer, Operating Systems, definition and types, Applications of MS Office for document creation & Editing, Data presentation, interpretation and graph creation, statistical analysis, mathematical expressions, Database, concepts and types, uses of DBMS in Agriculture, World Wide Web (WWW): Concepts and components, Introduction to computer programming languages, concepts and standard input/output operations. eAgriculture, concepts and applications, Use of ICT in Agriculture, Computer Models for understanding plant processes. IT application for computation of water and nutrient requirement of crops, Computercontrolled devices (automated systems) for Agriinput management, Smartphone Apps in Agriculture for farm advises, market price, postharvest management etc., Geospatial technology for generating valuable agriinformation. Decision support systems, concepts, components and applications in Agriculture, Agriculture Expert System, Soil Information Systems etc. for supporting Farm decisions, Preparation of contingent cropplanning using IT tools.

Mathematics for Applied Sciences
Content: Set theoryset operations, finite and infinite sets, operations of set, function. Vectors and vector spaces, Matrices notations and operations, laws of matrix algebra; transpose and inverse of matrix, Eigen values and Eigen vectors. Determinants  evaluation and properties of determinants, Solutions of Linear Equations. Variables and functions, limits and continuity of specific functions. Differentiation: theorems of differentiation, differentiation of logarithmic, trigonometric, exponential and inverse functions, Differentiation of function of a function, derivatives of higher order, partial derivatives. Application of derivatives, determination of points of inflexion, maxima and minima. Integration, methods of integration, reduction formulae, definite and indefinite integral, Applications of integration in Agriculture, Differential Equations.

Statistical Computing
Content: Introduction to statistical packages and computing: data types and structures, Use of Software packages like, SAS, SPSS or “R: The R Project for Statistical Computing”. Data analysis principles and practice, Summarization and tabulation of data, Exploratory data analysis; Graphical representation of data.Statistical Distributions: Fitting and testing the goodness of fit of discrete and continuous probability distributions; ANOVA, regression and categorical data methods; model formulation, fitting, diagnostics and validation; Matrix computations in linear models. Analysis of discrete data. Multiple comparisons, Contrast analysis. Numerical linear algebra, numerical optimization, graphical techniques, numerical approximations, Time Series Analysis. Analysis of mixed models; Estimation of variance components, Analysis of Covariance, Fitting of nonlinear model, Discriminant function; Principal component analysis. techniques in the analysis of survival data and longitudinal studies, Approaches to handling missing data, and metaanalysis.
Practical: Data management, Graphical representation of data, Descriptive statistics; General linear models ~ fitting and analysis of residuals, outlier detection; Fitting and testing the goodness of fit of probability distributions; Testing the hypothesis for one sample ttest, two sample ttest, paired ttest, test for large samples  Chisquares test, F test; One way analysis of variance, contrast and its testing, pairwise comparisons; Mixed effect models, estimation of variance components; Categorical data analysis, dissimilarity measures, similarity measures; Analysis of discrete data, analysis of binary data; Numerical algorithms; Spatial modeling, cohort studies; Clinical trials, analysis of survival data; Handling missing data; Analysis of time series data  fitting of ARIMA models.

Mathematical Foundations in Computer Science
Content: Mathematical Logic: Propositions – Simple and complex; Validity of PropositionTruth Tables; Use of Propositions in computer programming. Mathematical data types: Sets, Functions, Bijective functions, pigeonhole principle, Boolean functions, permutation functions, Boolean algebra, recursion relations. Number Theory: Binary arithmetic, exponentiation, induction, sequences, bigoh notation, GCD, Euclidean algorithm, partially ordered sets, congruence and equivalence relation, encryption scheme, Fibonacci sequence, linear homogenous recurrence relations with constant coefficients. Matrix Algebra Basic operations on matrices, Rank and inverse of matrices. System of linear equations, Characteristic roots and equations, Eigen values and eigen vectors; Graph Theory: Graphs, trees, LAN, Eulerian cycles, Hamiltonian cycles, graph coloring, graph algorithms.

Object Oriented Programming
Content: Introduction to Objected Oriented Programming(OOP), Introduction to C++, data types in C++, Compilation and execution of C++; data types, control flow, input/ output operations, interaction with file systems – reading, writing and appending. Strings, string manipulations, Arrays, functions, scope of variables, structures in C++. Classes, data members, member functions, this Pointer, Friends, Friend Functions, Friend Classes, Constructors, destructors. Operator Overloading, dynamic binding, parametric polymorphism. Inheritance, inheritance and dynamic binding, multiple inheritance. New Approaches to programming – ModelViewController (MVC) architecture, Single page applications.
Practical: Case studies using object oriented analysis and design (OOAD); Creation of classes with features  overloading, inheritance, data abstraction, polymorphism and Implementation of a case study.

Design and Analysis of Algorithms
Content: Algorithm Analysis – Time Space Tradeoff – Asymptotic Notations – Conditional asymptotic notation – Removing condition from the conditional asymptotic notation  Properties of bigOh notation – Recurrence equations – Solving recurrence equations – Analysis of linear search. Divide and Conquer: General Method – Binary Search – Finding Maximum and Minimum –Merge Sort – Greedy Algorithms: General Method – Container Loading – Knapsack Problem. Dynamic Programming: General Method – Multistage Graphs – AllPair shortest paths – Optimal binary search trees – 0/1 Knapsack – Travelling salesperson problem. Backtracking: General Method – 8 Queens problem – sum of subsets – graph coloring – Hamiltonian problem – knapsack problem. Graph Traversals – Connected Components – Spanning Trees – Biconnected components – Branch and Bound: General Methods (FIFO & LC) – 0/1 Knapsack problem – Introduction to NPHard and NPCompleteness.
Practical: Solving recurrence equations, Analysis of linear search, Programming Divide and Conquer Algorithms and their analysis, Programming Greedy Algorithms and their analysis, Implementing Dynamic Programming and their analysis, Implementing Backtracking examples, Implementing Graph Traversals, Implementing Spanning Trees.

Information Security
Content: General introduction to security, Cryptographic techniques: classical cryptography, conventional cryptography (DES), publickey cryptography (RSA), and digital signatures (DSA), steganography. Security services: message integrity, confidentiality and authentication, certification and key management (PKI). Network security applications: IP security (IPsec), Web security (SSL, TLS, SET), Electronic mail security (PGP, S/MIME), and SNMP security. Access control in computer networks: authentication protocols and services (Kerberos), firewalls and Virtual Private Networks (VPNs). System security: intrusion detection, viruses. Ecommerce securities: epayment systems, fair data exchange.

Web Technologies and Applications
Content: Survey of contemporary Internet Technologies  Role, use and implementation of currenttools. Application Layer Services and protocols  Domain name services, network management protocol, electronic mail and file transfer protocol. World Wide Web – Web pages, Web Sites, Web Servers; Intranet andxtranet Concepts; Web Application Architectures. Hyper Text Markup Language (HTML); Building static and dynamic web pages. Scripting Languages  Client side and server side scripting; Interaction with database. Latest trends in programming on the emerging technologies relating to web based software development.
Practical: Designing static website with features like tables, hyperlink among pages, pictures, frames and layers; Client side scripting for user interface validation; Server side scripting for database interaction; Designing of a information system.

Computer Networks
Content: The importance of Networking, Types of Networking, Network Topology, Transmission Media, Data communication: Concepts of data, signal, channel, bandwidth, bitrate and baudrate; Maximum datarate of channel; Analog and digital communications, asynchronous and synchronous transmission. Network adapters card, Multiplexer (FDM, TDM, STDM), Hub, Repeater. Network References Models: Layered architecture, protocol hierarchies, interface and services. ISOOSI references model, TCP/IP reference model; Data link layer function and protocols: Framing, errorcontrol, flow control; sliding window protocol; HDLC, SLIP and PPP protocol. Network layer  routing algorithms, congestion control algorithms; Internetworking: bridges and gateway; Transport layer  connection management, addressing; Flow control and buffering, multiplexing. Session layer – RPC; Presentation layer  abstract syntax notation. Application layer  File Transfer Protocol (FTP), Telnet, Simple Mail Transfer Protocol (SMTP); World Wide Web(WWW)  Wide Area Indexed Servers (WAIS), WAP; Network Security; Data compression and cryptography.

Data Structures
Content: Algorithms and analysis of Algorithms, Big Oh notation. Arrays, Linked Lists, Elementary List Processing. Memory Allocation for Lists. Strings. Compound Data Structures. Recursive algorithms, Divide and conquer, Dynamic programming, Trees, different tree traversal algorithms, graph traversal. Sorting, Selection Sort. Insertion Sort. Bubble Sort. Performance Characteristics of Elementary Sorts. Shellsort. Sorting Other Types of Data. Index and Pointer Sorting. algorithms. Quick sort, merging, merge sort, Heap structure, algorithm on heap structure, Queues, priority queues, Search Algorithms
Practical: Implementation of various types of structures  linked lists, doubly linked lists, circular linked lists, queue, dequeue, stack and tree; String processing; Searching and sorting techniques; Graph and geometric algorithms and Casestudies.

System Software and Programming
Content: Systems softwareintroduction, system specific features; Operating Systems and its functions – device management, process management, memory management, file system management, security. Users, directory, files, file access rights; Terminal Controls and signals; Modularization and program assembly – Interfaces, APIs, header files, libraries, shared objects, dynamic and static links. Input/output at System Level – sequential and random access; indexes. Memory Management –Allocating and deallocating memory; Threads, spawning processes, network access, sleep, Inter Process communications – pipes, shared memory, sockets, secured sockets, Certificates. Object oriented software design; Generic and reusable classes, Debugging and testing of programs
Practical: Low Level programming for input/output interface, memory, threads, listening and responding,; Programming constructs, control statements: branching and looping, file operations,; Creation of classes with features  overloading, inheritance, data abstraction, polymorphism and a case study using and Object oriented language.

Internet Technologies
Content: World Wide Web – Web pages, Web Sites, Web Servers; Intranet and Extranet Concepts; Hyper Text Markup Language (HTML); Building static dynamic web pages. Web application architecture – (ASP.NET/Java) – Web Forms, Server Side Controls, handling events, Validation, JQuery. Database Connectivity, read, write, update databases using web forms; data bound controls, sessions, session handling. Authentication of users, Personalization, Roles, role based access. Using external libraries/ controls; Ajax, Jquery; Data Exchange – XML, JSON; Creating web services.
Practical: Designing static website with features like tables, hyperlink among pages, pictures, frames and layers; Client side scripting for user interface validation; Server side scripting for database interaction; Designing of information system.

Bioinformatics Computing
Content: The Central Dogma, Review and Utilization of Biological Databases. Overview of Algorithms: Pattern Matching, Biological Motivation Naïve Algorithm. Preprocessing: Suffix trees Time and Space Considerations. Approximate Pattern Matching: Sequence Comparisons, Dot Plots. Sequence Alignment: Dynamic Programming, Global and Local Alignments Scoring Matrices, BLAST, FASTA Parameters. Similarity and Distance: PAM & BLOSUM matrices, Heuristic Approaches. Exhaustive Search Fragment Assembly: DNA Sequencing, Greedy Algorithms, Sequencing by Hybridization Fragment Assembly. Graph Algorithms, Overlap Graphs, and Hamiltonian Path Wrapup.
Practical: Suffix trees: Time and Space Considerations; Approximate Pattern Matching: Sequence Comparisons, Dot Plots; Sequence Alignment: Dynamic Programming, Global and Local Alignments Scoring Matrices, BLAST, FASTA Parameters; Similarity and Distance: PAM & BLOSUM matrices, Heuristic Approaches and Exhaustive Search Fragment Assembly: DNA Sequencing, Greedy Algorithms, Sequencing by Hybridization Fragment Assembly, Graph Algorithms, Overlap Graphs, and Hamiltonian PathWrapup.

Soft Computing Techniques
Content: Introduction to softcomputing tools – Fuzzy Logic, Genetic Algorithm, Neural Networks and Probabilistic Reasoning, Rough Sets. Applications of Fuzzy Logic concepts in Knowledge Management. Optimization problem solving using genetic algorithm. Neuron as a simple computing element, the perceptron, multilayer neural networks, Neural network approaches in data analysis, design and diagnostics problems; Applications of probabilistic reasoning approaches.
Practical: Classification using Fuzzy Logic, Genetic Algorithm, Neural Networks

Database Management System
Content: Database system  Operational Data, Characteristics of database approach, architecture. Overview of DBMS; Data associations  Entities, Attributes and Associations, Relationship among Entities, Representation of Associations and Relationship, Data Model classification. Entity Relationship model; Relational Data Structure Relations, Domains and Attributes, Relational Algebra and Operations, Retrieval Operations. Relational Database Design  Anomalies in a Database, Normalization Theory, and Normal forms; Query processing. Distributed Databases concepts, architecture, design; Structured Query Language (SQL)  Data Definition Language (DDL), Data Manipulation Language (DML). Unit VI PL/SQL  Stored procedure, Database triggers; Relational Data Base Management Package.
Practical: ER diagram construction; SQL  Command Syntax, Data types, DDL Statements, DML Statements, integrity constraints; Triggers, creating stored procedures/ functions; Normalization of database and Case study on a database design andimplementation.

Software Engineering
Content: Unit I Software engineering definition; Software Development: Phases, Process models, Project structure, Project team structure, Role of metrics, Measurement, Software quality factors. Planning and Software Project: Requirement analysis, Cost estimation, Project Scheduling, Quality Assurance Plan, and Project Monitoring Plans, Gantt charts, PERT and CPM. System Design: Design Objectives, Design Principles, Design Tools, and Techniques, Prototyping. Structured Programming Coding: Programming Practices, Verification, Monitoring and Control. Unit Testing: Testing Fundamentals, Functional Testing, Structural Testing, Test Plan activities, Unit testing, IntegrationTesting. Reliability: Concept of Software Reliability, Reliability Models, Limitations of Reliability Models, Software Maintenance. CASE tools.

Operating System
Content: Operating system overview: operating system as an extended machine and resource manager; Operating system classifications; Operating system modes and system calls. Operating system architecture; Process model, Process synchronization, Concurrent processes,Process scheduling criterion and algorithms. Problem of mutual exclusion; Deadlock and prevention; Race conditions; Semaphores; Monitors; Process allocation. Memory management; Multiprogramming with fixed and variable number of tasks; Continuousallocation; Paging, Demand paging, Page fault; Virtual memory; Fragmentation; Segmentedmemory management, shared segments; Segmented and demand paged management, Overlaysand swapping, Thrashing. Multiprocessor system, Master slave scheduling; Homogeneous scheduling; Device managementsystem; Dedicated share and virtual devices. File Management System InputOutput file protection; Remote Procedure Call; Distributedoperating system (Course to be taught in accordance to the Unix Operating System).
Practical: Problems using system calls for process management, signaling, file management, directorymanagement, protection; Critical section problem; Solution to mutual exclusion by Peterson method; Producer consumer problem with fatal race conditions; Comparison of various CPU scheduling algorithms and Paging, segmentation and demand paging.

Compiler Construction
Content: Introduction to Compiler, Compilation Process, Compiler Structure. Programming Language Grammars, Elements of a Formal Language Grammar, Derivation,Reduction and Syntax Trees, Ambiguity Regular Grammar & Regular Expression – Context FreeGrammar. Introduction to Finite Automata, Deterministic Finite Automata. Unit Nondeterministic Finite Automata; Scanning & Parsing Techniques – The Scanner, RegularGrammar and FSA, Top Down Parsing, Parsing Algorithm, Top Down Parsing WithoutBacktracking, Predictive Parsers, Bottom Up Parsing, Parsing, LR Parsers, Shift Reduce Parsing; Symbol Table. Organization, Memory Allocation – Static & Dynamic Memory Allocation, Compilation Control Transfer, Procedure Calls, Conditional Execution, Iteration Control Construct; Lexical Syntax Errors, Semantic, Major Issues in Optimization, Optimizing. Transformations, Local Optimization, Program Flow Analysis, Global Optimization.
Practical: Design of a lexical analyser for regular expression; Design of a finite state machine; Program for  magic squares, context free grammar, shift reduce parsing, operator precedence parsing, recursive decent parsing, predictive parser, simple LR parser and Post fix form for intermediate code.

Data Warehousing and Data Mining
Content: Concepts and principles of data warehousing; Project management and requirements. Introduction to Data Mining and its Tasks, Data Preprocessing, Data Discretization. Dimensional modelling; Data warehousing architecture; System process and process architecture. Classification and Prediction, Decision Tree, Naive Bayes’ Classifier. Data warehousing design; Database schema; Data staging. Output and Knowledge Representation, Evaluation and Credibility, Association Rule Mining. Partitioning strategy; Aggregations; Data marts; Meta data management; OLAP Modelling, Querymanagement. Clustering: Similarity measures, Hierarchical Clustering, kMeans Clustering. Data warehouse security; Backup and recovery; Building enduser Applications; Capacity planning; Testing the warehouse. Implementation and maintenance of data warehouse; Case study.
Practical: Data warehouse design, selection of schema; Normalization and renormalization; Query planstrategy; Performance tuning, backup and recovery of data warehouse; Dynamic reports and OLAP Reports. Introduction to Data Mining software, Data Preprocessing, Discretization, Decision Tree: D3,Naïve Bayes’ Classifier, Association Rule Mining: Apriori Algorithm, Clustering: Hierarchical Clustering, KMeans.