Course Credit: 4

Statistics is about extracting meaning from data. This course will introduce tech-niques to collect, present, summarize data in order to visualize the relationships indata and systematic techniques for understanding the relationships using mathe-matics.

Course Credit: 4

This is an introductory course on probability theory. This course attempts to provide basic concepts of set theory, experiment and sample space, and di erent approaches of de ning probability. It discusses useful laws of probability, conditional probability, Bayes rule, random variables and their distributions, and functions of random valu- ables. It also covers discussions on certain operators like mathematical expectation and generating function with properties and applications, and thorough discussions on commonly used probability distributions such as Binomial, Hyper geometric, Negative Binomial, Poisson, Normal, Exponential and Gamma distributions.

Course Credit: 3

Programming with C/C++ course mainly focuses the programming language C and the introductory part of Python. The course introduces machine level language, origin of C and application of C programming in statistics. Fundamental discussion on python program and its association with statistics have also been described in this course.

Course Credit: 3

Basic Algebra and Di erential Equation is the fundament of deriving statistical theories. Sound mathematical knowledge is pre-requisite to understand the basic concept of statistics. This course aims to introduce the number theories which are essential and widely used in the eld of statistics.

Course Credit: 4

The course starts by reviewing the properties of di erent functions and their graphs, limits, continuity and di erentiability. The derivative is used to solve related rates problems and the maximization-minimization problems which have a wide appli- cation in di erent eld of science and technology. This course also includes the techniques of integration, and use this to determine areas, lengths, surface area and volumes; multivariable calculus; including partial derivatives, multiple integrals, and their applications.

Course Credit: 4

This course provides an introduction to a broad range of economic concepts, descrip- tion of theories and analytical techniques. It considers both microeconomics: the supply and demand based choices made by individual units (households and rms), and macroeconomics: the broader analysis of the economy. The use of supply and demand models will be the fundamental tools and the trade-o s and choices will be considered through comparison of costs and bene ts measures. Production and market structure will be analyzed at the rm level as well. Macroeconomic issues concerning the interaction of goods and services markets, labor supply and economic resources at an aggregate level (region or country level) will be discuss

Course Credit: 3

Linear algebra can be used in pretty much any application that deals with more than random variable at a time than dealing with a random vector. Particularly, linear algebra will be heavily used in multivariate statistics course(s). Some matrix algebra will also be very convenient for potential studies in Markov chains and stochastic processes. Linear regression is a very common use of linear algebra as well.

Course Credit: 2

This course introduces Microsoft Excel as a tool for statistical analysis of data.

Course Credit: 2

This course consists of programming with C/C++ and linear algebra. It covers the practical applications of descriptive statistics and data analysis using C programming. In addition, matrices and vectors operations, eigenvalues, eigenvectors, Jordan decompositions have also been covered in linear algebra part.

Course Credit: 2

Each student must be examined by a committee of selected members at the end of the academic year.

Course Credit:

This is Non Credit Course. The aim of this course is to develop the students ability to use English e ectively for the purpose of practical communication. The course aims to expand vocab- ulary, increase pro ciency in reading, writing and listening and develop greater understanding of grammatical rules and usage. It therefore has four components: Writing, Reading, Listening and Speaking.

Course Credit: 4

This course introduces theory of probability and sampling distribution for describ- ing the behavior of random processes, and the concept of simulation of random processes. It also concentrates on the use of sampling distribution in statistical in- ference. The objective of this course is to familiar various probability distributions and sampling distributions. It includes deriving the properties of distributions, inter- relationship between them and focus on their applications for exploratory analysis regarding selection of appropriate distributions for explaining the random behavior based on data and statistical inference.

Course Credit: 3

This course aims at providing an introduction to principles of actuarial science and mathematics involved in it. Topics include role of insurance in economy, di er- ent instruments involved in actuarial computations including interest and discount rates, pricing and valuation of actuarial products including annuities, amortization schedules, sinking funds, life assurances and premiums, among others.

Course Credit: 3

This course deals with fundamental concepts and techniques of statistical inference including estimation and tests of simple and composite hypotheses. A brief revision will also be given of some basic topics in probability theory as well as random variables.

Course Credit: 3

This course covers methodological and practical issues to design and analysis of experiments. Topics covered will include an introduction to design of experiments, completely randomised design, randomised complete block design, Latin square de- sign and balanced incomplete block design.

Course Credit: 3

Demographic data contains valuable information about a countrys socio-economic development, population growth and trend as well as the overall public health sce- nario. Thus Demographic data comprises signi cant importance both in statistics and public health arena. This course focuses on de ning the demographic life events and explains the statistical analysis procedures.

Course Credit: 4

This course introduces basic sampling techniques used in sample survey along with estimation procedures. This course covers simple random sampling, systematic sam- pling, strati ed random sampling, and cluster sampling. It also covers some special sampling designs.

Course Credit: 3

This course presents mathematical and elements of scienti c computing approach for solving mathematical problems that arises mainly in science and engineering applications. It deals with the theory and application of numerical approximation technique as well as their computer implementation. Mathematical methods, mainly Fourier analysis and Laplace transform, provide a foundation for further studies in disciplines in which Mathematics and Statistics may play important roles.

Course Credit: 2

Any scienti c task without the knowledge of software is di cult to imagine and complete in the current scenario. R is a free software that is capable of handling mathematical and statistical manipulations. It has its own programming language as well as built in functions to perform any specialized task. Students intend to learn the basics of R software in this course, along with MATLAB/Octave.

Course Credit: 2

This course is designed to introduce computational methods and algorithms which will be helpful for understanding theoretical concepts and practical applications of probability and sampling distributions, and statistical inference including point and interval estimation and test of hypotheses.

Course Credit: 2

This course has two parts. First, students will apply their theoretical knowl- edge on Design and Analysis of Experiments to analyse practical data from completely randomized, randomized block and Latin square designs. Second, students will analyse practical data from simple random sampling, strati ed random sampling, systematic sampling and cluster sampling using their theo- retical knowledge on Sampling Methods.

Course Credit: 2

Each student must be examined by a committee of selected members at the end of the academic year.

Course Credit: 4

This course deals with the concepts and techniques used in the factorial design. The course examines how to design factorial experiments, carry them out, and analyze the data. Experiments with random factors and nested and split designs are also discussed in this course.

Course Credit: 3

This course introduces advanced sampling methods used in sample survey. It covers sampling of unequal clusters, two-stage sampling, multistage sampling, methods for estimating variance in complex surveys, and non-sampling errors.

Course Credit: 4

This introductory course gives an overview of regression types and details the appli- cation of multiple linear regression. This course covers the theory behind regression analysis, multiple linear regression, classical estimation and testing methods, and residual analysis. It also covers the formulation, interpretation and validation of linear regression models, and hands on use of a statistical package (SPSS) to see how the theory can be applied to answer a speci c research question.

Course Credit: 3

This course covers the basic principles and methods of epidemiology, with an em- phasis on critical thinking, analytic skills, and application to clinical practice and research. Topics include outcome measures, methods of adjustment, surveillance, quantitative study designs, and sources of data, analysis of exposure-disease rela- tionship and causal inference. The course will provide tools for critically evaluating the literature and skills to practice evidence-based medicine.

Course Credit: 3

The course introduces some of the demographic techniques to analyze population data and hence the case of population in Bangladesh.

Course Credit: 3

To infer means to make general statements on the basis of speci c observations. This is a task undertaken in almost every quantitative eld of research. This course deals with the advanced statistical inferential methods. It emphasizes concepts, methods and theory covering the broad topics of hypothesis testing, data reduction, likelihood inference in exponential families and criteria for comparison of estimators. Successful completion of this course will provide a foundation for understanding probability-based statistical inference material presented in other courses.

Course Credit: 3

The objective of the course is to introduce several useful multivariate techniques, and making strong use of illustrative examples.

Course Credit: 4

This course deals with the concepts and techniques used in the industry to maintain the quality of the process. Concepts of di erent types of control charts and their application in industry are discussed in this course. Basic concepts of acceptance sampling and how to design an acceptance sampling are also discussed. The con- cept and solution procedure of linear programming problem, duality and sensitivity analysis are discussed. Introduction of game theory, in particular the two person zero sum game are discussed.

Course Credit: 3

The study of mathematical analysis is indispensible for prospective students in dif- ferent sectors of graduate level, specially, in science, engineering, economics and management sciences. This course contains basic properties of real numbers, sets, intervals; sequences and series of real numbers; di erent properties of limits, conti- nuity, di erentiability and integration of a function; and basic concepts of measure theory and probability measure. Calculus is one of most fundamental courses in mathematical sciences and this is the prerequisite course for mathematical analysis.

Course Credit: 2

This is an introductory course of the three popular statistical packages. Students will learn to implement di erent statistical techniques using these packages.

Course Credit: 2

The objective of this course is to equip students with linear regression methods and models used in the analysis of factorial experiments with focus on the practical application of the methods. The computer exercises and project will need to use the free R software/language and the commands to produce the desired output and answer the relevant statistical questions. It includes least squares and maximum likelihood estimation, signi cance of variables, con dence intervals for model parameters, model selection and validation, residual analysis, and prediction.

Course Credit: 2

This computing course is based on the application of some statistical courses. Mainly the real life application of statistical inference, multivariate techniques, linear programming problem and life table models are discussed. In this course, problems related to hypothesis testing and data reduction are discussed. Application of multivariate normal distribution, inference about a mean vector, comparison of several multivariate means and multivariate linear regression models are discussed with real life data. Life table models and population projections are applied on real data sets to understand the solution procedure. The construction and solution procedures of linear programming problem are discussed.

Course Credit: 2

Each student must be examined by a committee of selected members at the end of the academic year. 53

Course Credit: 4

This module covers two important parts, one is advanced probability and another is stochastic processes. The module begins with a brief overview of modern probability and convergence of random variable and then moves on to the stochastic process to describe Markov chains, Poisson process, continuous time Markov chain and renewal process.

Course Credit: 3

A branch of statistics has been developed to draw conclusion in a short time and cost-e ective way regarding the population of interest which is ubiquitously known as statistical inference. It facilitates both parametric and nonparametric approaches under the umbrella of classical and Bayesian paradigms.

Course Credit: 4

Multivariate analysis skills have been recognized as part of the key requisites for statistical analysts. The complexity of most phenomena in the real world requires an investigator to collect and analyze observations on many di erent variables in- stead of a single variable. The desire for statistical techniques to elicit information from multivariate dimensional data thus becomes essential and crucial for data ana- lysts. This course focuses on multivariate methods based on normal theory. It gives students working knowledge on how to analyze data and solve problems involving measurements of p variables on each of n subjects.

Course Credit: 4

This course covers a range of econometric methods required to conduct empirical economic research and understand applied econometric results. Topics include mod- els for panel data, simultaneous equations models, models with lagged variables, and limited dependent variables.

Course Credit: 4

This course deals with the analysis of survival or failure-time data, which are commonly encountered in scienti c investigations. It is being extensively used in medicine, clinical trials, biological and epidemiological studies, engineering, nance and social sciences. This course provides an opportunity for students to learn sta- tistical lifetime probability distributions that are useful for modelling lifetime data. Topics includes lifetime distributions, non-parametric approaches, parametric mod- els and their estimation, accelerated failure time regression models, proportional hazard regression models.

Course Credit: 4

This course covers two important parts, one is research methodology and another is social statistics. Research methodology part includes theoretical concept of conduct- ing research, from problem identi cation to report writing. This part also discusses monitoring and evaluation techniques of intervention programs. Social statistics part includes measurement of national income, poverty and inequality, and psychometric measures.

Course Credit: 3

This is an introductory course of time series theory. The objective of this course is to equip students with various classical time series models, deriving their properties, inference methods and forecasting techniques for analyzing time series data. From computational point of view, it aims to demonstrate the theory with real datasets. Conclusions and proofs are given for some basic formulas and models; these enable the students to understand the principles of time series theory.

Course Credit: 3

This course deals with di erent statistical models for the analysis of quantitative and qualitative data, of the types usually encountered in research.

Course Credit: 2

A course designed to give hands-on experience of di erent inferential procedure. Students will learn to apply di erent multivariate techniques to solve real-life problems.

Course Credit: 2

Computing course on time series modeling and econometric modeling.

Course Credit: 2

This course deals with the applications of methods related to topics on gener- alized linear models and lifetime data analysis to real data from diverse areas such as medicine, biological science, engineering and social science. In addition, this course covers the use of statistical software, packages of each models for computing results from real data.

Course Credit: 2

Each student must be examined by a committee of selected members at the end of the academic year.

Course Credit: 3

Each student will be required to prepare a project report and present the report in a seminar. For the project work, each student will be assigned to a teacher at the beginning of the academic year. The project report submission and presen- tation should be made before the commencement of nal examination. The 50% weight of the course will be allotted to project work, 10% weight for supervisor and the remaining 40% weight will be for seminar presentation. The internal members of the examination committee will evaluate the performance in the seminars and the report will be evaluated by two internal examiners nominated by the examination committee. A supervisor cannot evaluate the project report s/he has supervised.