The DST offers Statistics for the Physical Science students and IS & MF students for their general degree degree program

Physical Science

Level I Course Units

Level III Course Units

SemesterPre ReqCourse UnitTitleCreditsHoursP1P2P3P4P5P6
IST 2006ST 3006Regression Analysis230Loxoxox
IST 3007Operational Research345Loxoxox
IST 2006ST 3009Applied Time Series230Loxoxox
IST 1011, ST 2006IS 3001Sampling Techniques230Loooooo
IIST 2008ST 3012Statistical Process Control230Loooooo

Combinations

  • P1 – Physics,Chemistry, Applied Math., Computer Science
  • P2 – Physics, Applied Math., Statistics, Computer Science
  • P3 – Physics, Applied Math., Pure Math., Computer Science
  • P4 – Chemistry, Applied Math., Statistics, Computer Science
  • P5 – Chemistry, Applied Math., Pure Math., Computer Science
  • P6 – Applied Math., Statistics, Pure Math., Computer Science

ST 1006 – Introduction to Probability and Statistics (2C, 30L)

Prerequisites: None

Intended learning outcomes:

Upon successful completion of the course the student should be able to describe data graphically and compute summary measures, compute probabilities by modeling sample spaces and apply rules of probability, construct the probability distribution of a random variable, expectation and variance, identify and compute probabilities based on practical situations using commonly used distributions.

Course content:
Descriptive Statistics: Types of data (qualitative, quantitative, continuous, discrete, etc.); scales of measurement (nominal, ordinal, interval, ratio); data summarization: frequency table, cum. frequency table, histogram, bar chart, pie chart, percentiles, quartiles, 5–number summary, Box plot, outliers; Measures of location: mean, trimmed mean, median, mode; Measures of dispersion: range, inter quartile range, variance, standard deviation, coefficient of variation; skewness, kurtosis; Counting techniques: counting rules, permutations and combinations; Elementary probability: probability definitions, finite sample space, events, probability rules and associated theorems, conditional probability, independence, multiplication rule, Bayes’ theorem; One dimensional random variables: probability density function and probability (mass) function, cum. distribution function, expected value and variance of functions of random variables, moment generating function; Probability distributions: discrete distributions (Uniform, Bernoulli, Binomial, Poisson) and applications; continuous distributions (Uniform, Exponential, Normal) and applications; central limit theorem with applications.

Method/s of evaluation: End of semester examination (70%) and Continuous assessment (30%)

References:

  • Introductory Statistics (Perm S. Mann)
  • Concise Course in A-Level Statistics (J. Crawshaw, J. Chambers)
  • Mathematical Statistics with Applications (Wackerly D. D., Mendenhall W. & Scheaffer R. L.)
  • Statistics for Business and Economics (Joseph G. Van Matre, Glenn H. Gillreath)
  • ‘Schaum’s Outline of Probability and Statistics – 4th Edition (M.R. Spiegel, J.J. Schiller and R.A. Srinivasan)

ST 1008 – Probability and Distributions (2C, 30L)

Prerequisites: None

Intended learning outcomes:
Upon successful completion of the course the student should be able to compute probabilities by modeling sample spaces and apply rules of probability, construct the probability distribution of a random variable, expectation and variance, identify and compute probabilities based on practical situations using commonly used distributions, and the central limit theorem.

Course content:
Introduction to probability; Counting techniques: counting rules, permutations and combinations; Elementary probability: probability definitions, finite sample space, events, probability rules and associated theorems, conditional probability, independence, multiplication rule, Bayes’ theorem; One dimensional random variables: probability density function and probability (mass) function, cum. distribution function, expected value and variance of functions of random variables, moment generating function; Probability distributions: discrete distributions (Uniform, Bernoulli, Binomial, Negative Binomial, Hypergeometric, Poisson, and Geometric) and applications; continuous distributions (Uniform, Exponential, Gamma, Chi-squared, Beta, Normal, t and F) and applications; central limit theorem with applications.

Method/s of evaluation: End of semester examination (70%) and Continuous assessment (30%)

References:

  • Introductory Statistics (Perm S. Mann)
  • Mathematical Statistics with Applications (Wackerly D. D., Mendenhall W. &Scheaffer R. L.)
  • ‘Schaum’s Outline of Probability and Statistics – 4th Edition (M.R. Spiegel, J.J. Schiller and R.A. Srinivasan)

ST 1009 – Exploratory Data Analysis (2C, 15L 30P)

Prerequisites: None

Intended learning outcomes:
Upon successful completion of the course the student should be able to explore and interpret data and draw meaningful conclusions using descriptive methods using one or more statistical software packages. The students should be able to clearly present statistical information, in both written and oral form.

Course content:
Picturing distributions with graphs: Individuals and variables, Categorical variables (pie charts, bar graphs), Quantitative variables (histograms, stemplots, time plots), picturing distributions with graphs using statistical software; Describing distributions with numbers: measuring center (mean, median, comparing the mean and the median), measuring spread (quartiles, five-number summary, boxplots, spotting suspected outliers, standard deviation), choosing measures of center and spread, describing distributions with numbers using statistical software; organizing a statistical problem; The Normal distributions: density curve, the 68–95–99.7 rule, the standard Normal distribution, finding Normal proportions; Relationships between two quantitative variables: explanatory and response variables, scatterplots, adding categorical variables to scatterplots, measuring linear association-correlation, facts about correlation, the best fitted line using least-squares, misuses of correlation and least square relationships; Relationship between two categorical variables: marginal distributions, conditional distributions, Simpson’s paradox; General Misuses of Statistics

Method/s of evaluation: End of semester examination (50%) and Continuous assessments [At least 3 in class assignments, 1 group project](50%)

References:
The Basic Practice of Statistics – 6th edition (Moore, Notz, Fligner)


ST 1010 – Statistical Theory (2C, 30L)

Prerequisites: ST 1008 – Probability and Distributions

Intended learning outcomes:
Upon completion of this course, students should be able to integrateadvanced concepts in probability and efficiently apply them for problem solving.

Course content:
Two–dimensional random variables: joint distribution (discrete, continuous), marginal and conditional distributions, independence, bivariate normal distribution, covariance, correlation, conditional expectation, expectation of functions of random variables; Bivariate transformations (discrete and continuous); Order statistics; Asymptotic theory.

Method/s of evaluation: End of semester examination (70%) and Continuous assessment (30%)

References:

  • Fundamentals of Mathematical Statistics (Gupta and Kapoor)
  • Mathematical Statistics with Applications (Wackerly, Mendenhall & Scheaffer)
  • Order Statistics (David)
  • Approximation Theorems of Mathematical Statistics (Serfling)

ST 1011 – Introduction to Surveys (2C,15L 30P)

Prerequisites: None

Intended learning outcomes:
Upon successful completion of the course the student should be able to solve a real life problem by properly planning and designing a survey focusing on selecting a sample scientifically.

Course content:
Producing data via surveys: Random & nonrandom sampling methods, cautions about sample surveys, planning and designing surveys, designing a questionnaire, pretesting, margin of error; Producing data via experiments: Randomized experimental methods, cautions about experiments; Solving a real world problem through a sample survey: Formulate a suitable research question, develop an appropriate sampling scheme, develop questionnaire, develop implementation plan, data collection, and analysis.

Method/s of evaluation:
End of semester examination (40%) and Continuous assessment [At least 2 in class assignments, 1 group project] (60%)

References:

  • What is a Survey (Fritz Scheuren)
  • Sampling Methods for Census & Surveys (Frank Yates)
  • Survey Methodology (Robert M. Groves, Floyd J. Fowler, Jr. et al)

ST 1012 – Basic Statistical Computing (2C, 15L 30P)

Prerequisites: None

Intended learning outcomes:
After a successful completion, a student should be able to perform data management using Excel, employ Excel functions, generate Recording and VBA Macros, and analyze data at exploratory level.

Course content:
Introduction to Excel, Manipulate worksheets, Import/Export files, Templates, Advanced formatting techniques, Excel functions, Database features, Pivot tables, Record Macros, Sub procedures in VB, VBA Macros, Data analysis tool pack.

Method/s of evaluation: Continuous assessments [At least 5 lab assignments] (100%)

References:

  • Excel for Engineers and Scientists (S. C. Bloch)
  • Microsoft Excel 2013 Bible (John Walkenbach)
  • Teach Yourself VISUALLY Excel 2007 (Nancy C. Muir)
  • Mastering excel macros: Introduction (Mark Moore)

ST 2006 – Basic Statistical Inference (3C, 45L)

Prerequisites: ST 1006 – Introduction to Probability and Statistics or ST 1008 – Probability and Distributions

Intended learning outcomes:
Upon completion of this course, students should be able to identify and compute probabilities based on sampling distributions and the central limit theorem, understand the theories of statistical inferences and apply theappropriate models in different settings to solve real-life problems, perform statistical inferences involving the mean, variance and proportion and goodness of fit tests.

Course content:
Sampling distributions, applications of central limit theorem; point estimation, bias and mean square error;
interval estimation, margin of error, determination of sample size; types of errors associated with hypothesis testing, power of the test, power curves; sampling from normal distributions, inferences about the mean and variance; large sample inference, inference for proportions; chi-square goodness-of-fit tests, chi-square tests for association.

Method/s of evaluation: End of semester exam (70%) and Continuous assessment (30%)

References:

  • The Basic Practice of Statistics – 6th edition (Moore, Notz, Fligner)
  • Mathematical Statistics with Applications (Wackerly, Mendenhall & Scheaffer)

ST 2007 – Applications in Statistical Inference (1C ,30P)

Prerequisites: ST 1008 – Probability and Distributions & ST 1010 – Statistical Theory

Intended learning outcomes:
Upon completion of this course, students should be able to use appropriate hypothesis tests and create interval estimations to solve real world problems using SPSS. The students should also be able to manage data within the SPSS platform.

Course content:
Introduction to SPSS, Data management, Application of central limit theorem, Inference about the mean/variance of a Normal population, Inference about means/variances of Normal populations – two sample problems, Inferences about the proportions, Chi-Square test for the goodness of fit /independence.

Method/s of evaluation: Continuous assessments (100%) [At least 4 lab assignments, 1 group project]

References:

  • Performing Data Analysis Using IBM SPSS (Lawrence S. Meyers,Glenn C. Gamst, A. J. Guarino)
  • Discovering Statistics Using IBM SPSS Statistics (Andy Field)

ST 2008 – Statistical Methods in Quality Control (2C, 30L)

Prerequisites: ST 1006 – Introduction to Probability and Statistics or ST 1008 – Probability and Distributions

Intended learning outcomes:
Upon successful completion of the course the student should be able to handle data using tools of Statistical Process Control (SPC), design and interpret variable and attribute type control charts by applying the basicsof control chart designs and sensitizing tools.

Course content:
Methods and philosophy of statistical quality control; Tools to enhance the quality of the process; Variable type control charts: x ̅ charts, R charts, S charts; Attribute type control charts: P charts, C charts, U charts; Control charts for short productions; Economic designs of control charts; Lot by lot acceptance sampling for attributes.

Method/s of evaluation: End of semester examination (80%) and Continuous assessment (20%)

References:

  • Introduction to Statistical Quality Control – 6th Edition (Douglas C. Montgomery )

ST 2004 – Analysis of Variance and Design of Experiments (2C, 30L)

Prerequisites: (ST 1006 – Introduction to Probability and Statistics or ST 1008 – Probability and Distributions) & ST2006 – Basic Statistical Inference

Intended learning outcomes:
After the successful completion, students should be able to identify the appropriate experimental design to suit the situation where a cause and effect relationship has to be established.

Course content:
Principles of design, Replication and randomization, Model for a completely randomized design, Analysis of variance for one–way classification, Standard errors for specific comparisons.

Method/s of evaluation: End of semester examination (80%) and Mid semester examination(20%)

References:

  • Design of Experiments: Statistical Principles of Research Design and Analysis – 2nd Edition (Robert O. Kuehl).
  • Design and Analysis of Experiments – 5th Edition (D.C. Montgomery )

ST 2009 – Applied Non Parametric Methods (2C, 30L)

Prerequisites: (ST 1006 – Introduction to Probability and Statistics or ST 1008 – Probability and Distributions) & ST2006 – Basic Statistical Inference

Intended learning outcomes:
After the successful completion, a student should be able to identify situations where non-parametric methods are applicable, select the appropriate non-parametric statistical method to apply for a particular problem, apply the method and find the solution for the research question.

Course content:
Introduction, One sample location tests, Tests involving two samples, Two independent sample tests for differences in location, Two independent sample tests for differences in spread, Two related samples, Tests involving more than two samples, Miscellaneous tests, Test of randomness, Tests using frequency data.

Method/s of evaluation: End of the semester examination (80%) and continuous assessment (20%)

References:

  • Practical Non-Parametric Statistics (William,Conover)
  • Applied Non-parametric Statistics – 2nd Edition (Wayne W. Daniel)
  • Non-Parametric Statistical tests based on ranks (Lehmann)

ST 2010 – Introduction to Statistical Modeling (1C, 15L)

Prerequisites: ST 1008 – Probability and Distributions

Intended learning outcomes:
Upon successful completion of the course the student should be able torecognize and use different forms of statistical models in the given context.

Course content:
Introduction to concept of Statistical Modeling, Building relationships between variables, Understanding the systematic and error components in modeling, Exploration of commonly used statistical models.

Method/s of evaluation: End of semester examination (80%) and Continuous assessment (20%)

References:

  • Statistical Methods in agriculture and biology (Roger Mead, Robert N Curnow and Anne M Hasted)


ST 3007 – Operational Research (3C, 45L)

Prerequisites: AM 2003

Intended learning outcomes:
Upon successful completion of the course, the students should be able to: describethe fundamental concepts of real world applications in operational research, model decision making problems, obtain solution/s for the formulated model/s using appropriate techniques and software packages.

Course content:
Integer Programming models and solution techniques (Cutting Plane algorithm, Branch-and-Bound algorithm), Zero-one Programming models (mind expanding problems),Transportation models and solution techniques (North-West Corner method, Least Cost method, Vogel’s Approximation method, U-V Method), Assignment models and solution techniques(Hungarian method), Inventory Models (Deterministic inventory models with shortages and without shortages),Queuing models (Elements of a queuing model, steady-state measures of performance, single-server models, multiple-server models),Solution techniques using suitable OR packages.

Method/s of evaluation: End of semester examination (70%) and Continuous assessment (30%)

References:
• Operational Research : An Introduction byHamdy A. Taha (6th Edition)
• Operational Research by A. P. Verma (3rd Edition)
• Operational Research by R. Panneerselvam
• Operational Research by Harvey M. Wagner
• Operational Research by Hillier and Liebermann


ST 3008 – Applied Statistical Models (3C, 30L 30P)

Prerequisites: ST 2010

Intended learning outcomes:
After successful completion of the course the student should be able to analyze and interpret categorical and continuous data using appropriate linear and non-linear models using SAS/R. The student should also be able to use appropriate model diagnostic tools to validate the fitted models.

Course content:
Introduction to modeling. Continuous models with fixed effects: Simple Linear Regression, Multiple Linear Regression, Non Linear Regression. Data categorization. Contingency table analysis. Categorical models with fixed effects: log linear models, logistic models, Polytomous regression, Ordinal response models, Nominal response models, Analysis of categorical data using a SAS/R, Interpreting parameter estimates, Goodness of fit test. Introduction to random effects and mixed models.

Method/s of evaluation: End of semester examination (70%), Continuous assessment (20%) and Case studies/ Group project (10%)

References:
• Regression analysis by example (Chatterjee S., Price B.)
• Categorical data analysis (Agresti A.)
• An introduction to generalized linear models (Dobson A.J., Barnett A.G.)
• Applied mixed models in medicine (Brown H., Precott R.)
• Modeling binary data (Collet D.)


ST 3009 – Applied Time Series (2C, 30L)

Prerequisites: ST 2006

Intended learning outcomes:
After successful completion of the course the student should be able to use appropriate univariate time series models for forecasting.

Course content:
Introduction: Areas of application, Objectives of time series analysis, Components of time series, Descriptive analysis. Distributional properties: Independence, Autocorrelation, Stationary. Probability models to time series: Random walk, Autoregressive model. Moving Average model, mixed models, parameter estimation, Diagnostics. Forecasting: Optimal forecasts, Forecasts for ARMA models, Exponential Smoothing forecasting method.

Method/s of evaluation: End of semester examination (80%) and Continuous assessment (20%)

References:
• Forecasting Methods and Applications -3rd Edition (Makridakis, S. Weelwright, S. C. and Hyndman, R. J.)
• The analysis of Time Series: An Introduction – 6th Edition (Chatfield, C )
• Forecasting and Control – 4th Edition (Box, G. E. P., Jenkins, G. M. and Reinsell, G. C)


ST 3010 – Introduction to Health Statistics (2C, 15L 30P)

Prerequisites:

Intended learning outcomes:
At the end of the course the students should be able to define and compute official health statistics and construct life tables. Compute suitable descriptive statistics. Construct confidence intervals. Carryout hypothesis tests, calculate sample sizes. Identify Data Science approaches to health data. Analyze and interpret health data using statistical package/s.

Course content:
Introduction to official health Statistics: Mortality, Crude death rate, Standardization, Morbidity, Incidence and prevalence. Introduction to Life tables and applications. Descriptive statistical methods for health data (Summary statistics), Inferential methods (confidence intervals, hypothesis testing) for health data, Sample size calculation. Introduction to Data science for health statistics. Health data analysis using statistical packages.

Method/s of evaluation: End of semester examination (70%) and Continuous assessment [minimum of 2 In-class assignments and Case studies] (30%)

References:
• Statistical methods in medical research – 4th Edition (Armitage P.,Berry G., Mathews J.)
• Practical Statistics for medical research (Altman D.G)
• An Introduction to medical statistics – 3rd edition (Bland J.M)
• SAS for Data Analysis (Marasinghe M. G.)


ST 3011 – Statistical Programming (2C, 60P)

Prerequisites:

Intended learning outcomes:
After a successful completion a student should be able to plot 2D and 3D graphs using Python /R; write Python/R functions to solve statistical problems ; perform data analysis using Python /R

Course content:
Introduction to Python. Built-in data types, Arrays and Matrices, Basic Math using Python. Basic functions and Numerical indexing, Special arrays. Advanced selection and Assignment, Flow control, loops and exception handling. Graphics using Python. Introduction to R; Data Management, Descriptive Analysis, Writing functions in R; Statistical Inference.

Method/s of evaluation: Continuous assessments [At least 5 lab assignments] (100%)

References:
• An Introduction to R – Version 3.1.2 (2014-10-31) (W.N.Venables, D.M. Smith and the R Core Team)


ST 3012 – Statistical Process Control (2C, 30L)

Prerequisites: ST 2008/ MS2001

Intended learning outcomes:
Upon successful completion of the course the student should be able to investigate and analyze process capability, advanced charts and control charts for correlated data. The student should also be able to recognize the statistical quality control methods using acceptance sampling, response surface approach for optimizing the process.

Course content:
Capability analysis; Cumulative Sum (CUSUM) control charts; Exponentially Weighted Moving Average (EWMA) Charts; Acceptance sampling: double, sequential, multiple; Decision theory approach; Multivariate control charts; Process optimization with design experiment.

Method/s of evaluation: End of semester examination (80%) and Continuous assessment (20%)

References:
• Introduction to Statistical Quality Control – 6th Edition (Douglas C. Montgomery)
• Quality Control and Industrial Statistics (A. J. Duncan)


ST 3013 – Essential Mathematics for Statistics (3C, 45L)

Prerequisites:

Intended learning outcomes:
Upon the successful completion of the course the students should be able to apply basic mathematical tools in solving theoretical and practical problems in Statistics.

Course content:
Linear algebra: Linear dependence, rank and the solution of homogeneous equations, characteristic polynomials, eigenvalues , eigenvectors, spectral theorem for symmetric matrices, idempotent matrices and properties, orthogonal projections, trace of a matrix and properties, positive definite/semi definite matrices, quadratic forms, differential calculus in matrix notation, direct product (kronecker) of any two matrices, generalized inverse /conditional inverse; Caculus1: Concepts of functions, limits and continuity, L’Hopital’s rule, the fundamental theorem of calculus, approximation of definite integrals, Improper integrals; Series and Sequences: sequences and their convergence, series and convergence of series, power series and their convergence of radius, Taylor series and their application; Several variable calculus: functions of several variables, continuity, differentiability, derivatives, multiple integrals, change of variables

Method/s of evaluation: End of semester examination (70%) and Continuous assessment (30%)

References:
• Calculus and Linear Algebra. Vol. 1: Vectors in the Plane and One-Variable Calculus (Wilfred Kaplan; Donald J. Lewis)
• Linear Algebra Done Right [Undergraduate Texts in Mathematics] (Sheldon Axler)
• Calculus (Ron Larson and Bruce H. Edwards)


IS 3001 – Sampling Techniques (2C, 30L)

Prerequisites: IS 1009/ST 1011, ST 2006

Intended learning outcomes:
Upon the successful completion of the course the students should be able to identify and effectively use the theory behind sampling techniques that are commonly used in statistics.

Course content:
Simple Random Sampling (SRS), Sample size determination, Ratio and Regression estimators under SRS, Stratified, Systematic, and Quota sampling. Separate and combined estimators for stratified sampling. Cluster sampling, Multi-stage sampling, Complex sample designs and related issues.

Method/s of evaluation: End of semester examination (70%) and Continuous assessments & group projects (30%)

References:
• Sampling – 2nd Edition (Steven K. Thompson)
• Practical sampling Techniques (Ranjan Kumar Som)
• Hand book of Sampling Techniques and Analysis (Poduri S.R.S. Rao, Myron J. Katzoff)


IS 3004 – Applied Multivariate Methods (2C, 15L 30P)

Prerequisites: ST 2006

Intended learning outcomes:
Upon successful completion of this course the students should be able to apply the related multivariate techniques to data with multiple measurements satisfying the underlying theories and assumptions. The students should also be able to demonstrate basic computer skills in analyzing such data with the help of appropriate statistical packages.

Course content:
Overview, examples and introduction. multivariate normal distribution ; mean vector and variance covariance matrix, correlation matrix; bivariate normal distribution and density, missing values and outliers, summary statistics, standardized data; sample correlations; multivariate data plots, checking for multivariate normality; eigen-values and eigenvectors; geometric descriptions; principal components analysis, factor analysis, discriminant analysis, cluster analysis; multivariate inference; inference for one and two independent samples, profile analysis, repeated measurements, manova, canonical variates analysis, canonical correlations.

Method/s of evaluation: End of semester examination (70%) and Continuous assessment (30%)

References:

• Applied Multivariate Methods for Data Analysts (1998) (D.E. Johnson)
• Applied Multivariate Statistical Analysis – 3rd Edition (1992) (R.A. Johnson and D.W. Wichern)
• Computer Aided Multivariate Analysis (Afifi, Clark, and May)

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IS 3005 – Statistics in Practice I (3C, 90P)

Prerequisites:

Intended learning outcomes:
After the successful completion of this course, a student should be able toemploy the complex process of problem-solving a massing various areas in the field of statistics. Students should be able to formulate the problems, improve on report-writing and research skills, their communication, personnel and business skills.

Course content:
This course deals with general principles involved with statistical methods covered levels I, II in solving real-life statistical problems.

Method/s of evaluation: 100% based reports on a minimum of 2 Case studies/mini projects, presentations based on seminars, in-class assignments, attendance

References:
• Problem Solving: A Statistician’s Guide – 2nd edition (Chris Chatfield)


MS 3004 – Quality Management/Project Management (2C, 30L)

Prerequisites:

Intended learning outcomes:
Upon successful completion of the course students should be able to apply key theoretical concepts on quality control and project management practiced by the corporate world.

Course content:
Quality Management: Macro and Micro organizational Environment (PESTEL, Resource Based View)Market Analysis(Porter’s Five Forces Analysis, SWOT Analysis) Project Feasibility Analysis ( Johnson & Schole’s SFA Framework) Stakeholder Analysis, Organizational Change Management; Project Management: Project Selection, Approach Selection, The Work Breakdown Structure, The Network Diagram, Cost Effort Estimation, Optimizing the Network, Gantt Chart, Risk Management, Cost Estimation, Contract Management, Productivity Improvement, Project Management Steps, Making the Budget, Project Monitoring and Control, Human Resource Management, Project Termination.

Method/s of evaluation: End of semester examination (70%) and Case studies[minimum of 2]and presentations (30%)

References:
• Fundamentals of Project Management (Joseph Heagney)


MS 3009 – Operational Research II (3C, 30L 30P)

Prerequisites: MS 1003

Intended learning outcomes:
Upon successful completion of the course, the students should be able to describe the fundamental concepts of real world applications in operational research, model decision making problems, obtain solution/s for the formulated model/s using appropriate techniques and software packages.

Course content:
Network Models (Minimal Spanning Tree algorithm, algorithms for Shortest-Route problem, Maximal Flow model), Project Planning (Critical Path Method, Programming Evaluation and Review Technique), Inventory Models (Deterministic inventory models with shortages and without shortages, Probabilistic Inventory models), Queuing models (Elements of a queuing model, steady-state measures of performance, single-server models, multiple-server models), Solution techniques using suitable OR packages.

Method/s of evaluation: End of semester examination (70%) and Continuous assessment (30%)

References:
• Operational Research : An Introduction – 6th Edition (Hamdy A. Taha)
• Operational Research – 3rd Edition (A. P. Verma)
• Operational Research (R. Panneerselvam)
• Operational Research (Harvey M. Wagner)
• Operational Research (Hillier and Liebermann)