Make students realize about understanding and importance of the data along with the summarizing and presenting the data in tabular / diagrammatic manner, using measures of central tendencies and dispersions.

In this semester, students will learn basic concepts of probability and various discrete probability distributions such as binomial, poisson, hypergeometric and uniform distribution.

In this semester, students will learn correlation and regression analysis for bivariate data, time series analysis and index numbers.

In this semester, students will learn various continuous probability distributions such as rectangular, exponential, normal and chi-square distributions. Also concept of point and interval estimation, large sample test is introduced.

In this course, student will be able to develop problem-solving techniques needed, to accurately calculate, probabilities using moment generating functions and cumulant generating functions. Most widely used discrete probability distributions such as binomial, poisson, uniform, geometric, negative binomial, and hypergeometric, truncated binomial and truncated poisson and recognize them in applications. Understand concept of bivariate probability distributions and Jacobian transformation.

In this course, student will be able to understand the objectives of a sample survey. Know the common sampling techniques such as simple random sample, stratification, systematic sampling, cluster sampling; recognize and understand when it is appropriate to use each technique.

In this course, a student will be able to formulate linear programming problem (LPP) in standard form, and use of the simplex method to solve them. Use duality to solve LPP. Understand optimization techniques in transportation, assignment, sequencing problems. Understand decision making under uncertainty and risk.

In this course, a student will be able to understand of basic concepts in time series analysis. Understand various forecasting techniques and knowledge on modern statistical methods for analyzing time series data.

In this course, student will be able to recognize the importance of the central limit theorem and understand when it is appropriate to use normal approximations for the distribution of a statistic. Most widely used continuous probability distributions such as rectangular, normal, beta type I and II, gamma, chi-square, t and F and recognize them in applications. Construct exact and approximate confidence intervals.

In this course, student will be able to understand the principles of randomisation, replication, local control and theory of designing experiments. Understand and use the terminology of experimental designs. Explore the general theory of factorial and block designs.

In this course, a student will be able to understand the role and application of CPM, PERT for project scheduling. Understand the concepts of statistical process control and quality control. Understand the concept of data mining.

In this course, a student will be able to understand some basic concepts of research and its methodologies. Understand basic concepts of sampling. Write a research report and thesis.

In this course, a student will be able to calculate probability of events by using laws of probability. Derive probability distribution of order statistics and sample range, sample median.

In this course, a student will be able to fit various continuous probability distributions and to study various real life situations.

In this course, a student will be able to apply different methods of estimation and be able to select the best estimator based on various properties of the estimator.

In this course, a student will be able to identify appropriate sources of data, perform basic demographic analyses using various techniques and ensure their comparability across populations. And to able to produce population projections and interpret the information gathered by the different demographic methods.

In this course, a student will be able to fit the simple, Multiple and Logistic Regression models. Model building, residual diagnostics, corrective measures and polynomial regression model. Test the hypothesis of model parameters, AIC and BIC criteria. Interpret the output produced by glm command in R.

In this course, a student will be able to understand basic econometric techniques needed for empirical quantitative analysis.

In this course, a student will be able to make correct decisions in real life market circumstances. Estimate the no. of units to be kept in stock keeping in view the cost constraints in various situations.

This course introduces students to general block designs with particular cases and importance of confounding in factorial designs. Apply BIBD & Split-Plot design in appropriate situations. Justify the use of total and partial confounding and analyse the design accordingly.

In this course, a student will be able to write c- programs. Write control and looping statements. Construct c-user-defined functions and c-structures.

This course is to acquaint students with the concepts such as Survival analysis, Reliability theory, Censoring and Non-parametric estimation of Survival function. By the end of this course, learner will able to Find survival functions and hazard functions from the survival data. Compute reliability of the system. Understand different types of censoring. Compute K-M estimator of survival function.

In this course, a student will be able to define and distinguish between various types of Parametric and nonparametric methods. Formulate test-statistic formula when sample size is not fixed in advance and also for fixed sample size.

In this course, a student will be able to construct different types of stochastic processes and queuing models. Students will able to differentiate various types of birth and death processes. Identify Markov processes and Markov chains. Setup different types of queuing models.

In this course, a student will be able to differentiate different types of annuities, assurance plan. To calculate present and accumulated value of money under different types of annuities. To calculate and compare level annual premium under different assurance plan.

In this course, a student will be able to do data preparation for knowledge discovery, Data Processing ,Data Cleaning, : Data Mining Process: CRISP and SEEMA.

In this course, a student will be able to determine rate at which infection spreads for a given epidemic. Evaluate statistically the significance of the treatments given. Formulate appropriate study design to estimate different parameters and analyse the results.

In this course, a student will be able to understand applications of time series in forecasting using statistical methods. Distinguish different components of time series.

In this course, a student will be able to construct linear models with the help of matrix theory. Analyse a Co-variance matrix.

In this course, a student will be able to write simple R-commands to calculate various statistical measures. Write R-commands for testing of hypothesis and ANOVA.