Undergraduate Program

Course Credit: 4

Course Description This course deals with fundamental concepts and methods of statistics as a whole. The impact that statistics has made and will continue to make in Data Science and virtually in all elds of scienti c and human endeavors will be illustrated. A brief discussion on the introductory de nitions and meaning of terms and terminologies used in Applied Statistics and Data Science will be given. The basic topics in data, variables, their measurements, storage, management and methods for analyzing them will be presented. Course Objectives The aim of the course is to provide a thorough theoretical grounding in data summarization and description for nding patterns in data. The primary objective is to provide an introduc- tion to summary measures necessary for the subsequent study of core and specialized courses in statistics and data science, namely, statistical machine learning, data science, probability, regression, generalized linear models, multvariate techniques, time series, econometrics and associated computing software. Course Learning Outcomes (CLOs) Given the aims and objectives of the course, the course learning outcomes of the course can be summarized as illustrated here. After completion of the course, students will be able to CLO1 develop a deeper understanding of the underlying features of the concepts of data science and modern methods of descriptive statistics CLO2 equip students with founding principles and application of data summarization tool kit CLO3 develop foundational basis of ithe theories of applied statistics and data science so that they can apply their knowledge and skills to study other courses of the discipline CLO4 possess the ability to use fundamental statistical knowledge, methodologies and modern data science tools in a suitable and pertinent way CLO5 possess the ability to apply excel software to diverse data summarization techniques 17 CLO6 contextualise outputs where data are generated from diverse and evolving social, political and cultural dimensions and from diverse digital foot-prints CLO7 possess the skill to comprehend the balance between the di culty and correctness of the data summary measures used and the equitability of the delivery of the solution and the ability to interpret the solution in plain language CLO8 demonstrate presentation skill and independent comprehensive ability and an abil- ity to amalgam knowledge and compliantly apply them to characterise, analyse and solve a wide range of problems. CLO9 understand statistical good practice and ways to evade possible abuse or misuse of statistical summary. CLO10 to apply the principles of lifelong learning to any new challenges. Program Learning Outcomes (PLOs) CLOs PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8 CLO1 3 CLO2 3 CLO3 3 CLO4 3 CLO5 2 CLO6 3 CLO7 3 CLO8 2 CLO9 2 CLO10 2

Course Credit: 4

Course Description : 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 valuables. 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, hypergeometric, and negative binomial, Poisson, normal, exponential and gamma distributions. 19 Course Objectives To provide basic concepts of sets, counting techniques, and acquaint students with necessary skills for solving probability related problems using appropriate laws. To introduce the notions of random variables. To develop ability to nd probability distribution of random variables and of their functions. To introduce operators like generating functions, expectation, etc. for studying the characteristics of distributions. To make familiar with basic probability distributions with possible areas of applications. To prepare the students for learning advance courses where probability theory has a prominent role. Course Learning Outcomes (CLOs) Students who successfully complete this course should be able to CLO1 understand (explain ideas and concept) Students will be able to explain basic con- cepts of set theory, experiment and sample space, di erent approaches of de ning probability, and useful laws of probability. CLO2 analyze (draw connection among ideas) Students will be able to analyze and draw connections between concepts such as conditional probability, Bayes rule, random variables, and their distributions. CLO3 evaluate (justify a stand or decision) Students will be able to justify their decisions by evaluating the probability distributions of random variables and the use of operators like mathematical expectation and generating functions. CLO4 apply (use information in new situation) Students will be able to apply the concepts and laws of probability to solve problems and nd probability distributions of random variables in new situations. CLO5 create (produce a new or original work) Students will be able to create new insights and original work by using generating functions and studying characteristics of various probability distributions. Program Learning Outcomes (PLOs) CLOs PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8 CLO1 3 2 3 2 1 2 1 1 CLO2 2 3 3 3 2 2 1 1 CLO3 2 2 3 3 2 3 1 1 CLO4 2 2 2 3 2 3 1 1 CLO5 2 2 3 3 2 2 2 1 Contents Combinatorial analysis: basic principles of counting, permutations, combinations; axioms of probability: sample space and events, axioms of probability, sample spaces having equally likely outcomes, probability as a measure of belief; conditional probability and independence: conditional probabilities, Bayes formula, independent events. 20 Random variables: introduction, discrete random variables, expectation, expectation of a function of a random variable, variance, Bernoulli and binomial random variables, Poisson random variable, other discrete random variables (geometric, negative binomial, hypergeo- metric); expected value of a sums of random variables; properties of cumulative distribution function; continuous random variables: expectation and variance of continuous random vari- able, normal random variable, normal approximation to binomial distribution, exponential random variables. Jointly distributed random variables: joint distribution functions, independent random vari- ables, sums of independent random variables, conditional distributions (discrete and contin- uous cases); properties of expectation: expectation of sums of random variables, covariance, variance of sums, correlations, conditional expectation, moment generating functions, proba- bility generating function.

Course Credit: 3

Course Description: The Foundation of Mathematics course is meticulously designed to provide a thorough ex- ploration of three fundamental pillars in mathematics: algebra, di erential equations, and discrete mathematics. This course covers the theory of numbers, theory of equations, se- ries, formulation of simple applied problems in terms of di erential equations and solving approaches, discrete mathematical structures, such as sets, graphs, and combinatorics to pre- pare students for developing problem-solving skills by applying mathematical concepts to real-world problems, and enhancing analytical thinking and logical reasoning skills. Course Objectives The objective of the course is to equip students with a solid understanding of the fundamental concepts, theories, and applications of these mathematical areas so that students can develop a strong foundation for advanced topics in statistics and computing. Learning Outcomes After completion of the course, students are expected to CLO1 learn and understand the theory of numbers, di erential equations and discrete mathematics in detail 21 CLO2 understand and apply di erent mathematical structures, such as sets, graphs, and combinatorics, to solve problems in computer science, cryptography, and other related elds CLO3 develop problem-solving skills by applying mathematical concepts to real-world problems CLO4 to enhance analytical thinking and logical reasoning skills. Program Learning Outcomes (PLOs) CLOs PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8 CLO1 3 3 3 CLO2 2 2 2 3 3 CLO3 1 1 1 3 3 1 CLO4 2 3 2 2 Contents Part A (Basic Algebra) Theory of numbers: unique factorization theorem; congruences; Euler's phi-function; inequal- ities: order properties of real numbers; Weierstrass', Chebysev's and Cauchy's inequalities; inequalities involving means; complex numbers: eld properties; geometric representation of complex numbers; operations of complex numbers; summation of algebraic and trigonometric nite series; theory of equations: relations between roots and coe cients; symmetric functions of roots; Descartes rule of signs; rational roots; Beta and gamma function and their properties; incomplete beta and gamma function ; Dirich- let's theorem. Part B (Di erential Equations) Formulation of simple applied problems in terms of di erential equations; equations of the rst order and their solutions; singular solutions; geometric applications; linear equations with constant coe cients; method of undetermined coe cients; variation of parameters, simple cases of linear equations with variable coe cients. Part C (Discrete Mathematics) Combinatorics: Counting principles. Inclusion-exclusion principle. Pigeonhole principle. Generating functions. Recurrence relations. Graphs, structure, and symmetry of graphs, trees and connectivity, Eulerian and Hamiltonian graphs and diagraphs, directed graphs, and planar graphs. Algorithms on graphs: Introduction to graphs, paths and trees. Shortest path problems: Di- jkstra'a algorithm, Floyd-Warshall algorithm and their comparisons. Spanning tree problems. Kruskal's greedy algorithm, Prim's greedy algorithm and their comparison. *In addition to the compulsory question, students should answer at least one question from each of the part.

Course Credit: 3

Introduction: The course is designed to provide a comprehensive foundation in the concepts and techniques of calculus. Key topics include basic functions, their graphs, and properties, as well as curve sketching, limits, continuity, di erentiation, and relative extrema. Applications of di erenti- ation and the Taylor Series are also covered. Additionally, the course expands calculus from the two-dimensional world of single-variable functions into the three-dimensional world and beyond, addressing multivariable functions.This course aims to equip students with a robust understanding of both single-variable and multivariable calculus, preparing them for advanced mathematical and analytical challenges. Objectives This course aims to develop both computational pro ciency and a deep geometric understand- ing of calculus, equipping students with essential mathematical skills for advanced studies in statistics. Emphasizing the ability to perform complex calculations accurately and visualize geometric interpretations of functions and surfaces. Students will learn to apply these tech- niques to real-world problems and statistical analyses, including curve tting and di erential equations. Learning Outcomes Upon completion of the course, students will be able to CLO1 execute di erentiation for single-variable and multivariable functions, and apply these techniques to solve real-world problems CLO2 understand and analyze the geometric interpretations of calculus, including the be- havior of functions, shapes of curves, and properties of surfaces in three-dimensional space. CLO3 employ optimization techniques, including the use of Lagrange multipliers, to solve complex problems involving maxima and minima in various contexts. 23 CLO4 cultivate a logical and systematic approach to problem-solving, enhancing their ability to tackle advanced mathematical challenges in academic and professional settings. Program Learning Outcomes (PLOs) CLOs PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8 CLO1 2 3 1 3 3 CLO2 2 3 1 3 2 CLO3 2 3 1 3 2 CLO4 2 3 3 3 2 Contents Functions and their graphs: polynomial and rational functions, logarithmic and exponential functions, trigonometric functions and their inverses, hyperbolic functions and their inverses, composition of functions. Limit and Continuity of Functions: De nition. Basic limit theorems, limit at in nity and in- nite limits. Continuous functions. Properties of continuous functions on closed and bounded intervals. Di erentiability and related theorems: Tangent lines and rates of change. De nition of deriva- tive. One-sided derivatives. Rules of di erentiation. Successive di erentiation. Leibnitz the- orem. Related rates. Linear approximations and di erentials. Rolle's theorem, Lagrange's and Cauchy's mean value theorems. Extrema of functions, problems involving maxima and minima. Concavity and points of in ection. Indeterminate forms. L'Hospital's rule. Power series expansion: Taylor's theorem with general form of the remainder; Lagrange's and Cauchy's forms of the remainder. Taylor's series. Maclaurin series. 5. Applications: Physical, Biological, Social Sciences, Business and Industry. Vector-valued functions: Introduction to Vector-Valued Functions, Calculus of Vector-Valued Functions, Tangent lines to graphs of vector-valued functions. Arc length from vector view point. Arc length parameterization. Curvature: Unit Tangent, Normal and Binormal Vectors, Curvature of plane and space curves: Curvature from intrinsic, Cartesian, Parametric and Polar equations. Radius of curvature. Centre of curvature. Partial Di erentiation: Functions of several variables, Graphs of functions of two variables, Limits and continuity, Partial derivatives, Di erentiability, linearization and di erentials. The Chain rule. Partial derivatives with constrained variables, Directional Derivatives and Gradi- ents, Tangent Planes and Normal Vectors, Extrema of functions of several variables, Lagrange multipliers. Taylor's formula for functions of two variables.

Course Credit: 3

Course Description: This course introduces students to Integral Calculus, focusing on the techniques of integration and their applications to solving physical problems. Key topics include nding areas under curves, lengths of curves, volumes, and surface areas using integration methods. Students will gain a comprehensive understanding of integral calculus concepts and develop the skills needed to apply these techniques to various mathematical and real-world scenarios. Course Objectives The course aims to equip students with a robust understanding of Integral Calculus and its applications. By the end of the course, students will be able to interpret de nite integrals geometrically as areas under curves and construct them as limits of Riemann sums. They will understand di erentiation and anti-di erentiation as inverse operations, as outlined in the Fundamental Theorem of Calculus, and will be pro cient in evaluating integrals using various techniques. Additionally, students will be able to recognize and solve di erent types of improper integrals, calculate areas between curves, volumes of solids of revolution, surface areas, and arc lengths using integration. They will also develop the ability to solve problems involving multiple integrations in rectangular, cylindrical, and spherical coordinate systems, and apply appropriate models. Learning Outcomes Upon completion of the course, students will be able to CLO1 interpret the de nite integral geometrically as the area under a curve and construct de nite integrals as the limit of Riemann sums. CLO2 select and apply appropriate models and techniques to de ne and evaluate line and surface integrals. CLO3 solve problems involving multiple integrations using rectangular, cylindrical, and spherical coordinate systems. CLO4 calculate areas between curves, volumes of solids of revolution, surface areas, and arc lengths using integration. 25 Program Learning Outcomes (PLOs) CLOs PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8 CLO1 1 3 2 3 1 CLO2 2 3 2 3 3 CLO3 2 3 2 3 3 CLO4 1 3 1 3 3 Contents The anti derivatives (inde nite integral): Techniques of integration; integration by parts; integration by substitution; integration of rational functions, Integration by reduction. De nite integration: using antiderivatives. De nite integration using Riemann sums. Funda- mental theorems of calculus. Basic properties of integration. Applications of integration: Plane areas. Solids of revolution. Volumes by cylindrical shells. Volumes by cross-sections. Arc length and surface of revolution. Improper integrals: Improper integrals of di erent kinds. Gamma and Beta functions. Integrals in polar coordinates: Area and arc length in polar coordinates. Double Integrals: Double Integrals over Nonrectangular Regions, Double Integrals in Polar Coordinates, Surface Area; Parametric Surfaces and Applications of Double Integrals. Triple integrals: Volume as a triple integral, Triple Integrals in Cylindrical and Spherical Coordinates, Centers of Gravity Using Multiple Integrals and Applications of Triple Integrals, Change of Variables in Multiple Integrals; Jacobians. Topics in vector calculus: Vector Fields, Gradient, Divergence, curl and their physical mean- ings Line Integrals, Green's Theorem, Surface Integrals, The Divergence Theorem, Stokes' Theorem, Applications of Surface Integrals; Flux.

Course Credit: 3

Course Description: Linear algebra can be used in pretty much any application that deals with more than one random variable at a time rather than dealing with a random vector. Particularly, linear 26 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 Objectives To solve systems of linear equations using various methods, including Gaussian and Gauss- Jordan elimination and inverse matrices. To perform matrix algebra, determinants, and their properties. To understand and apply properties of real vector spaces and subspaces, including linear independence and dependence. To nd eigenvalues and eigenvectors and use them in applications. Learning Outcomes Upon completion of MATH 106, students will be able to CLO1 solve systems of linear equations using Gaussian and Gauss-Jordan elimination and inverse matrices CLO2 Perform matrix algebra, determinants, and understand their properties (Concept). CLO3 Understand and apply properties of real vector spaces and subspaces, including linear independence and dependence CLO4 Find eigenvalues and eigenvectors and apply them in various scenarios (Applica- tion). CLO5 Create orthogonal and orthonormal bases using the Gram-Schmidt process and solve application problems Program Learning Outcomes (PLOs) CLOs PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8 CLO1 1 2 3 3 3 CLO2 3 3 2 CLO3 3 3 3 CLO4 2 3 2 CLO5 2 3 3 2 Contents Matrices, vectors and their operations: basic de nitions and di erent types of matrices, matrix operations (addition, multiplication), trace of a matrix, determinant and adjoint of a square matrix, properties of determinants, inverse of matrix, properties of inverse, Kronecker product and related operations. System of linear equations: Gaussian elimination, Gauss-Jordan elimination, homogeneous linear systems, null spaces and the general solution of linear systems, rank and linear systems, generalized inverse of a matrix, generalized inverses and linear systems. Vector spaces and subspaces: vector addition and scalar multiplication, linear spaces and subspaces, intersection and sum of subspaces, linear independence and dependence, basis and dimension, inner product, norms and orthogonality, orthogonal projections, Gram-Schmidt orthogonalization. Eigenvalues and eigenvectors: eigenvalue equation, characteristic polynomial and its roots, Eigenspaces and multiplicities, diagonalizable matrices, computation of eigenvalues and eigen- vectors. Singular value and Jordan decompositions: singular value decomposition, SVD and linear systems, computing the SVD, Jordan canonical form. Quadratic forms: matrices in quadratic forms, positive and nonnegative de nite matrices, congruence and Sylvester's law of inertia, nonnegative de nite matrices and minors, some in- equalities related to quadratic forms, simultaneous diagonalization and the generalized eigen- value problem.

Course Credit: 4

Course Description: This course provides an introduction to a broad range of economic concepts, description of economic laws and theories, and analytical reasoning behind those. 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 discussed. Course Objectives The objectives of the course are to introduce students to principal microeconomic theories and demonstrate how these laws can be used to explain economic choices made by households and rms; to describe how macroeconomic concepts can be used to analyze economy as a whole; to help students learn how to interpret economic models, diagrams and tables and use them to analyze di erent economic phenomena; and to illustrate how government policies in uence microeconomic choices and macroeconomic upshots. Learning Outcomes After completion of the course, students are expected to 28 CLO1 understand theories and principles in microeconomics including consumer theory, production cost, price theory, market structure, trade-o s, comparative advantage, factor markets, public goods, externalities and market failure. CLO2 learn macroeconomic concepts and principles including productivity, in ation, em- ployment and government economic policies. CLO3 acquire quantitative skills used in economic modelling and analysis. CLO4 understand scal and monetary policies of Bangladesh and how they are related to relevant SDG goals. CLO5 demonstrate the skills to apply and explain macroeconomic and microeconomic principles to analyse di erent individual, rm or state-level economic issues. Program Learning Outcomes (PLOs) CLOs PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8 CLO1 3 CLO2 3 CLO3 2 3 2 1 CLO4 1 2 2 3 2 1 CLO5 1 1 3 1 3 3 1

Course Credit: 2

Course Description: Programming with C/C++ course mainly focuses on the programming language C. The course introduces machine level language, origin of C and applications of C programming in statistics. Course Objectives This module covers introductory ideas to connect C programming with statistics through programming code execution. To understand how to analyze descriptive statistics and data analysis using C programming. Learning Outcomes After completion of the course, students are expected to CLO1 be introduced with the C programming language. CLO2 know techniques that facilitate the development of structured computer programs, such as algorithms, pseudocode, owchart, etc. Understand how to control C programs, write C functions, use arrays. CLO3 how to connect C programming with statistics through programming code execu- tion. CLO4 understand how to analyze descriptive statistics and data analysis using C pro- gramming. Program Learning Outcomes (PLOs) CLOs PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8 CLO1 3 CLO2 3 CLO3 2 1 CLO4 3 1

Course Credit: 2

Course Description: In today's data-driven world, business enterprises and industries can access more data than ever. However, they need skilled professionals who can manage and analyze data using various tools and techniques to make sense of all this data. Structured Query Language (SQL) is a widely used tool in data science for managing and querying data stored in relational databases. This course is designed to introduce students to SQL and its application in data science. Course Objectives To teach students the basics of SQL through hands-on exercises and real-world examples. To cover advanced topics such as SQL joins, subqueries, and aggregation functions. To provide students with a comprehensive understanding of SQL and its pivotal role in data science. Learning Outcomes Upon completion of CSE-131, students will be able to CLO1 understand the basic concepts of SQL, such as tables, queries, and basic SQL syntax (Concept). CLO2 retrieve and manipulate data from databases using SQL queries, including ltering, sorting, and joining data from multiple tables (Application). CLO3 perform basic data analysis using SQL, including calculating summary statistics and aggregating data (Compute, Application). CLO4 understand how SQL ts into the data science work ow (Concept). 33 Program Learning Outcomes (PLOs) CLOs PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8 CLO1 3 1 2 1 2 CLO2 1 2 3 3 CLO3 3 3 2 CLO4 3 Contents Introduction to databases, SQL and MySQL, creating a MySQL database, MySQL Data types, table creation, Populating and modifying tables, When Good Statements Go Bad, query mechanics. The select, from, where, group by, order by, and having clauses. SQL joins, subqueries, common table expressions, aggregation functions, window functions, date and time functions, and user-de ned functions (UDFs). Calculating summary statistics, univariate and bivariate frequency distributions, correlation, and regression analysis.

Course Credit: 2

Course Credit: 4

Course Description Understanding a nation's population patterns, socioeconomic growth, and public health all depend heavily on its demographic data. To analyze this data, this course de nes demographic life events and describes statistical analysis methodologies. Course Objectives The course is intended to introduce the basic ideas of demography and state the importance of demographic studies. It helps to understand well-known demographic theories and di erent measures of demographic events. Students will perceive and analyze current demographic situation of Bangladesh.

Course Credit: 3

Course Description Sampling refers to the statistical techniques used in survey research in many areas such as business, marketing, environmental science, political polls, and many areas of social science and public health. This course introduces di erent sampling techniques and approaches to designing a sample survey and methods for analyzing survey data from both a theoretical and applied perspective. This course covers topics related to general procedures in sample surveys, some basic sampling techniques, sample size determination, and methods of estimation of parameters of interest under each sampling scheme. Course Objectives The objectives of the course are to make students familiar with di erent sampling techniques, sample size determination, and estimating the parameters of interest and their use in practice, and to prepare students to demonstrate their skills for designing all basic sample surveys, analyzing the data, and explain them to various stakeholders and other researchers in diverse areas.

Course Credit: 3

Course Description The objectives of the course are to develop an understanding of the core ideas and concepts of numerical methods, to enable students to apply rigorous analytic and highly numerate approaches, and to develop scienti c computer programs and to equip students with the ability to apply Fourier series and Fourier integrals to signi cant applied problems, particularly in telecommunication, which are closely related to Laplace transform. Course Objectives The objectives of the course are: To develop an understanding of the core ideas and concepts of numerical methods. To enable students to apply rigorous analytic and highly numerate approaches, and to develop scienti c computer programs. To equip students with the ability to apply Fourier series and Fourier integrals to signi cant applied problems, particularly in telecommunication, which are closely related to Laplace transform.

Course Credit: 3

Course Description Graduate students in applied statistics need to know mathematical analysis. Real numbers, sets, intervals, sequences and series, limits, di erentiability, continuity, integration, measure theory, and probability are all covered in this subject. Prerequisites for this study include a basic mathematical science course called calculus. Course Objectives The course has great value for a student who wishes to go beyond the routine techniques to solve standard problems, and who wants to extend ideas to a new context. It develops the ability to analyze mathematical situations properly and precisely. It also helps the students to learn how to work comfortably with concepts that initially seemed so mysterious.

Course Credit: 2

Course Description 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. Course Objectives The objective of the course are to teach the basics of R programming language that includes understanding di erent types of R objects, writing functions, reading/writing data of di erent formats, basic programming tools, and creating di erent types of plots.

Course Credit: 2

Course Description This course is designed to introduce computational methods and simulation techniques which that will be helpful for understanding theoretical concepts and practical applications of prob- ability and sampling distributions, and statistical inference including point and interval esti- mation and test of hypotheses. R programming is used to demonstrate related simulations and computations. Course Objectives The main objective of this course is to introduce students to the theory of simulation and illustrate to them the theoretical concepts of sampling distributions and statistical inference using computer-simulated data. The course also aims at introducing students to compu- tational methods and statistical software (R programming) required for tting probability distributions, conducting point and interval estimation, and performing test of hypotheses using real-life datasets.

Course Credit: 2

Course Description This computing course will discuss the applications of the statistical methods discussed in the courses design of experiment and demography. In addition, this course demonstrates the use of statistical software R to apply the models related to di erent experimental designs to 50 analyze the data in the eld of agriculture, industry, and medicine. Moreover, this course helps students to learn, analyze and interpret real-life demographic data. Course Objectives The course's main objective is to teach students to use statistical software (e.g., R) and analyze experimental design and demographic data. The speci c objective is to provide hands on learning by incorporating practical exercises and real-world examples to equip students with the knowledge and skills needed to excel in conducting scienti c research or working in industry.

Course Credit: 2

Course Credit: 3

Course Description This course provides a comprehensive introduction to sampling distributions-a fundamental concept in statistical theory that underpins the process of making inferences and drawing conclusions from sample data. Sampling distributions play a critical role in statistical analysis by providing a bridge between sample data and population parameters. Contents will involve deriving the theoretical properties of di erent distributions, examining the inter-relationship between them and focusing on their applications in statistical inference. Course Objectives This course's main objective is to acquaint students with the main concepts and uses of probability and sampling distributions. More speci c objectives include demonstrating the derivation of sampling distributions of sample statistics, describing properties of common sampling distributions and illustrating the role and use of sampling distributions in inferential statistics.

Course Credit: 3

Course Description This course aims at providing an introduction to principles of actuarial science and mathemat- ics involved in it. Topics include role of insurance in economy, di erent instruments involved in actuarial computations including interest and discount rates, pricing and valuation of actu- arial products including annuities, amortization schedules, sinking funds, life assurances and premiums, among others. Course Objectives The course will enable understanding of the fundamental concepts of actuarial science and appreciate its role in economy. It will build a concrete knowledge of underlying theories behind actuarial computations. Finally, it will help to understand how statistical and mathematical models are used in pricing and valuing actuarial products and their real life applications.

Course Credit: 4

Course Description 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 Objectives The aim of the course is to provide a thorough theoretical grounding in statistical inference. The primary objective is to provide an introduction to mathematical statistics necessary for the subsequent study of specialized courses in statistics, bio-statistics, actuarial science and econometrics. The impact that statistics has made and will continue to make in virtually all elds of scienti c and other human endeavors is considered.

Course Credit: 3

Course Description The design of an experiment involves considering the set of treatments selected for compari- son, specifying the units to which the treatments are to be applied, determining the rules for allocating treatments to units, and specifying the measurements to be made on each unit. Var- ious designs are now standard in a wide range of practical situations in agriculture, medicine, engineering, and other elds. This course covers methodological and practical issues in the design and analysis of experiments. Topics covered include an introduction to the design of experiments, completely randomized design, randomized complete block design, Latin square design, and balanced incomplete block design. Course Objectives The course aims to help students understand the methodological issues of experimental design. Students will develop the skills to write hypotheses that can be tested using experiments and to analyze and interpret data obtained from experiments.

Course Credit: 4

Course Description This course explores the concepts and techniques involved in factorial design. It covers how to design factorial experiments, conduct them, and analyze the resulting data. Additionally, the course addresses experiments with random factors, as well as nested and split-plot designs. Course Objectives The main objectives of the course are to teach the basic ideas of factorial design; design experiments involving up to 2 and 3 factors with k levels; analyze the data from such exper- iments; interpret the results of any analysis; and design and analyze experiments involving random factors. Additionally, the course aims to teach the methods involved in designing and analyzing experiments with k factors, each having two levels and three levels.

Course Credit: 3

Course Description The main objectives of this course is to acquaint students with the methodologies associated with the advanced sampling techniques. 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. 53 Course Objectives To acquaint students with the methodologies associated with the advanced sampling tech- niques and their application in real survey.

Course Credit: 4

Course Description This introductory course provides an overview of di erent types of regression and describes the use of linear regression. This course covers the theory underlying regression analysis, simple and multiple linear regression, classical estimation and testing methods, and residual analysis. In addition, it includes the formulation, analysis, interpretation and validation of linear regression models as well as the practical application of a statistical package to demonstrate how the theory can be applied to address a particular research question. Course Objectives Students are expected to learn the techniques of linear regression models and they should understand applications of the linear regression models in real life situations.

Course Credit: 3

Course Descriptions This course serves as an introduction to epidemiology, the study of the distribution and de- terminants of health-related states or events in populations, and the application of this study to control health problems. Epidemiology is a cornerstone of public health, providing essen- tial tools for understanding disease patterns and developing e ective interventions. Topics include measures of disease occurrence, study designs, exposure-disease relationship, control of extraneous factors and interaction. Course Objectives The course aims to equip students with the knowledge, skills and abilities necessary to under- stand disease patterns, identify risk factors, and contribute to evidence-based decision-making in public health and healthcare settings. It also emphasizes critical thinking, problem-solving, and ethical conduct in epidemiological research and practice.

Course Credit: 3

Course Description This course deals with the analysis of time-to-event data (also known as survival or failure time data), which are commonly encountered in scienti c investigations. It is being exten- sively used in medicine, clinical trials, biological and epidemiological studies, engineering, economics, and social sciences. This course provides an opportunity for students to learn life- time probability distributions that are useful for modeling tine-to-event data. Topics include lifetime distributions and non-parametric and parametric approaches for analyzing time-to- event data. Course Objectives The course's primary focus is to make students familiar with the situation where censored data appear and to be able to analyze such data by implementing appropriate methods and models. Secondly, to introduce statistical theory and methodology for analyzing lifetime data obtained from censored samples, emphasizing statistical lifetime distributions, types of censoring, graphical techniques, and non-parametric/parametric estimation.

Course Credit: 3

Course Description To infer means to make general statements based on 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 Objectives The objective of this course is to provide a thorough theoretical grounding and working knowledge of statistical inference. It aims to teach fundamental materials which will be of use in specialized courses in statistics such as actuarial science and biostatistics. At the end of this course, one should be able to perform hypothesis tests, explain the principles of data reduction, judge the quality of estimators as well as choose appropriate methods of inference to tackle real problems.

Course Credit: 3

Course Description The course provides a deeper knowledge within multivariate statistics, both in theory and applications. Multivariate distributions with emphasis on the multivariate normal distribution and its properties are considered. The course also provides deeper studies of the inference theory in multivariate analysis, for example concerning the mean and covariance matrices. The concepts that are more thoroughly treated in the course are: multivariate analysis of variance, pro le analysis, multivariate regression analysis. Matrix algebra is used through the course as a necessary tool for the inference as well as computer sessions. An overview about necessary concepts from matrix algebra useful for the course will be given. 62 Course Objectives Multivariate data analysis helps in the reduction and simpli cation of data as much as possible without losing any important details. As MVA has multiple variables, the variables are grouped and sorted on the basis of their unique features. The variables in multivariate data analysis could be dependent or independent. It is important to verify the collected data and analyze the state of the variables. In multivariate data analysis, it is very important to understand the relationship between all the variables and predict the behavior of the variables based on observations. It is tested to create a statistical hypothesis based on the parameters of multivariate data. This testing is carried out to determine whether or not the assumptions are true.

Course Credit: 3

Course Description The research methodology course is fundamental to teaching students the theoretical and practical skills required to conduct research in various elds. The course introduces students to the fundamental principles of research design, data collection, data analysis, and report writing. It will cover a range of research approaches, including quantitative, qualitative, and mixed methods. Course Objectives The course aims to provide students with hands-on experience in designing and conducting research studies, particularly to develop students' knowledge and understanding of how to formulate research questions, develop hypotheses, choose appropriate research methods, and 64 collect and analyze data and write a report for scienti c publication. In addition, the course is designed to provide students with the knowledge and skills necessary to conduct valid, reliable, and ethical research.

Course Credit: 2

Course Description This course is designed to provide students with a comprehensive understanding of using Stata-a powerful statistical software package-for data science applications. Students will learn how to leverage Stata's capabilities to explore, analyze, and visualize data, with a focus on practical skills relevant to data science and statistical analysis. Objectives The objectives of the course are to empower students with practical Stata skills and analytical competencies essential for conducting data-driven research and making informed decisions in various academic, professional and research settings. The course emphasizes hands-on learning of data entry, management, analysis and e ective communication of ndings.

Course Credit: 2

Course Description Python has become one of the most popular programming languages for data science due to its simplicity, versatility, and robust data analysis and machine learning capabilities. As organizations continue to digitize and generate increasingly large amounts of data, open- source tools like Python can enable data manipulation and exploration, machine learning, and statistical analysis in a scalable manner. This course provides a gentle introduction to programming in Python and its applications in data science and analytics. Objectives To give students a working knowledge of Python programming. To teach programming fun- damentals, basic data structures, writing functions, and importing and exporting data in di erent formats. To cover data manipulation and data visualization using Python. To pre- pare students for more advanced courses in data science and machine learning.

Course Credit: 2

Course Description This statistical computing course provides a comprehensive understanding of applications of the methods covered in the courses design and analysis of experiments, epidemiology, and lifetime data analysis to real-life problems, equipping students with the tools and knowledge to address real-life problems across various elds. By achieving these skills, students are expected to make data-driven decisions and contribute to solving the complex challenges faced by society today. Course Objectives This course aims to equip students with the skill to use statistical software (e.g., R, Stata, or Python) to apply the methods discussed in the courses design and analysis of experiments, epidemiology, and lifetime data analysis. Through these skills, students are expected to tackle problem-solving challenges while working in industry, agency, or academia, make data-driven decisions, and contribute to planning and policy.

Course Credit: 2

Course Description This computing course is based on applications of regression models, multivariate methods, and statistical inference to real-life problems. By mastering these techniques for solving real- life problems, students will be equipped to analyze complex datasets, uncover meaningful insights, and contribute to data-driven decision-making processes in academia, industry, and research. Objectives The objectives of this computing course are to teach students the applications of some sta- tistical methods (regression models, multivariate methods, and statistical inference) using statistical software (R, Python, or Stata) and interpret the results. More speci cally, the idea is to engage students in hands-on exercises and projects that involve real-life datasets, provid- ing practical experience in applying regression models, multivariate methods, and statistical inference techniques to address real-world problems.

Course Credit: 2

Course Credit: 3

Course Description 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 variables and then moves on to the stochastic process to describe Markov chains, Poisson process, continuous time Markov chain and renewal process. Course Objectives To develop a deep understanding of the advanced concepts in probability theory and stochastic processes. To apply these concepts to solve real-world problems in various domains. To pre- pare students for research and professional practice in elds requiring sophisticated stochastic modeling.

Course Credit: 4

Course Description The course provides an introduction to the eld of Arti cial Intelligence and a broad but thorough introduction to the methods and practice of basic statistical machine learning and its core methods, models, and algorithms. This course is about providing students of ap- plied statistics and data science with detailed knowledge of how the basic Machine Learning methods work for arti cial intelligence and how statistical models can be brought to bear in computer systems not only to analyze large, high-dimensional, and big data sets but also 73 to let computers perform tasks, that traditional methods of computer science are unable to address. Course Objectives After completing the course students will be familiar with the concept of Arti cial Intelligence, Big Data, and their areas. It describes philosophies and techniques of Arti cial Intelligence, including intelligent agents, and search. It also describes several models for supervised and unsupervised machine learning and regularized modeling techniques, assess the strengths and weaknesses of each of these models, know the underlying mathematical relationships within and across statistical learning algorithms, develop and implement optimization methods for training of statistical models, design decision and optimal control problems to improve the per- formance of statistical learning algorithms, design and implement various statistical machine learning algorithms in real-world applications, evaluate the performance of various statisti- cal machine learning algorithms, demonstrate a working knowledge of dimension reduction techniques. identify and implement advanced computational methods in machine learning.

Course Credit: 3

Course Description Multivariate analysis skills are recognized as part of the key requisites for statistical analysts. The complexity of most real-world phenomena requires investigators to collect and analyze observations on many di erent variables rather than a single variable. The need for statistical techniques to extract information from multivariate data thus becomes essential and crucial for data analysts. This course focuses on multivariate methods based on normal theory. It provides students with working knowledge on how to analyze data and solve problems involving measurements of p variables on each of n subjects. Course Objectives The objective of this course is to provide students with experience in using multivariate techniques for analyzing research data. The aim is to teach students how to select appropriate methods for multivariate data analysis and interpret the results e ectively.

Course Credit: 4

Course Description This course covers a range of econometric methods required to conduct empirical economic research and understand applied econometric results. Topics include models for panel data, simultaneous equations models, models with lagged variables, and limited dependent variables. Course Objectives The objectives of the course are to introduce students to the basic principles of econometric analysis; to help students gain theoretical understanding of the statistical models needed for econometric research including their underlying assumptions, advantages and limitations and to help them understand how to apply di erent econometric tools to analyze real-world economic problems and interpret ndings

Course Credit: 3

Course Description Lifetime data (also known as survival or failure time data) are commonly observed in health science, engineering, and social sciences. This course deals with di erent approaches of mod- eling lifetime data. The theoretical backgrounds and related practical applications of both parametric and semi-parametric approaches of analysing lifetime data are discussed in the course. A brief introduction to competing risks and frailty models is also provided. Course Objectives The course primarily focuses on familiarizing students with regression models commonly used to analyze time-to-event (lifetime) data. Both the accelerated failure time (AFT) model and Cox's proportional hazards (PH) model will be discussed in detail in the course. Students will also learn about the methods for analyzing competing risks models and correlated time- to-event data.

Course Credit: 3

Course Description This course is designed to introduce key computational concepts, tools and techniques for cu- rating, managing, and analyzing data of large volume, various types and di erent frequencies. This course assumes basic exposure to the concepts of arti cial intelligence, machine learning algorithms, deep learning and computer programming. Course Objectives The principal aim of this course is to introduce Big Data and its characteristics and challenges to students, and to teach them appropriate tools for managing and analyzing such large-scale data. Further objectives include helping students understand applications of Big Data in di erent elds and also ethical issues related to Big Data.

Course Credit: 3

Course Description This course serves as an introduction to time series theory. Its objective is to equip stu- dents with various classical time series models, including deriving their properties, inference methods, and forecasting techniques for analyzing time series data. From a computational standpoint, the course aims to demonstrate theory with real datasets. Conclusions and proofs are provided for some basic formulas and models to enable students to understand the prin- ciples of time series theory. Course Objectives Upon completion of the course, students should become acquainted with the main concepts of time series theory and methods of analysis. They should understand the di erences be- tween cross-sections and time series and grasp the fundamental advantages and necessity of appropriate forecasting methods in particular environments. Additionally, students should be pro cient in using the statistical software R to analyze time series data.

Course Credit: 3

Course Description This course is about introducing statistical methods including the general linear model for quantitative responses (including multiple regression, analysis of variance, and analysis of covariance), binomial regression models for binary data (including logistic regression and probit models), and models for count data (including Poisson regression and negative binomial models). All of these techniques are covered as special cases of the Generalized Linear Model, which provides a central unifying statistical framework for the entire course. Course Objectives After completing the course students will be familiar with a uni ed procedure of several mod- eling techniques. They will be familiar with the exponential family of distributions, the class of generalized linear models (GLM) as regression models with responses from the exponential family of distributions, the concepts of link functions for modeling the correspondence between the expected value of the responses and covariates and of variance functions for specifying the correspondence between the expected values and variances of the responses, analyzing data from important special cases of GLMs, in particular logistic regression and Poisson regres- sion, and extensions of the GLM framework using quasi-likelihood based on speci ed link and variance functions.

Course Credit: 3

Course Description O cial statistics is a branch of statistics that deals with the collection, analysis, interpretation, and dissemination of data produced by government agencies and other o cial bodies. Its purpose is to provide accurate and reliable information on various aspects of society, such as the economy, population, health, education, and crime. O cial statistics are used for various purposes, including policy-making, research, business-decision making etc. Course Objectives The objectives of the course are to make students familiar with di erent sources of o cial statistics and their institutional, legal, and ethical aspects, and to prepare students with strong methodological basis for measuring, processing, producing, and using o cial statistics for national policies and planning.

Course Credit: 2

Course Description This course provides hands-on experience with di erent inferential procedures and multivari- ate techniques, focusing on their application to real-life problems. Students will learn to use statistical software (R package) to analyze data using principal component analysis, factor analysis, discriminant analysis, and canonical correlation analysis. The course aims to equip students with the skills necessary to interpret and present their ndings clearly. Course Objectives The objectives of the course are to provide students with hands-on experience in applying di erent inferential procedures and multivariate techniques using statistical software (R pack- age).Teach students how to extract and present the most important features from complex data. Finally enable students to interpret analysis results and prepare scienti c publications.

Course Credit: 2

Course Description This statistical computing course focuses on real-life applications of the topics covered in the courses: time series analysis and econometrics methods. The course will equip students with the tools and techniques to analyze and interpret time series, and economic and nancial data, make forecasts, estimate economic relationships, and contribute to economic research and policy analysis by integrating statistical software R, Stata, or Python. Course Objectives The course's main objective is to familiarize the students with statistical software and packages (R, Stata, or Python) to apply time series and econometric methods for analyzing data and solving real-life problems. The aim is to prepare students with the skills and knowledge necessary to pursue careers in economics, nance, data analytics, or research institutions where time series analysis and econometrics are essential.

Course Credit: 2

Course Description This course deals with the implementation of GLMs and survival models to real-life data from diverse areas such as medicine, biological science, engineering, and social science. In addition, this course demonstrates the use of statistical software and packages (e.g., R or Stata) to compute the models using real-life data. Course Objectives The objectives of the course are to teach students the application of a broader range of statistical models to analyze real-life data from diverse areas. Secondly, to equip students with skills to use statistical software and packages (e.g., R or Stata) to compute the models and discuss the models and results with other researchers and stakeholders.

Course Credit: 2

Course Credit: 3