‏This course introduces the fundamental concepts of statistics, focusing on data collection, organization, presentation, and analysis using descriptive and probabilistic statistical measures. The course covers measures of central tendency, dispersion, and shape, as well as correlation and regression, in addition to basic probability concepts and probability distributions, enhancing students’ ability to perform statistical analysis and support decision-making.

This course introduces fundamental mathematical concepts, including sets and operations on them, intervals and their types, inequalities, absolute values, real functions and their types, domain and range of functions, limits, continuity, differentiation, and applications of calculus, with an emphasis on establishing a solid mathematical foundation for students.

This course introduces students to the fundamentals of computer programming and programming languages. It covers programming concepts, stages of program development, algorithm design and representation, flowcharts for sequences, branches, and loops, control statements, as well as arrays, their types, and basic operations on them.

‏This course provides students with a general overview of the fundamentals of the Arabic language, focusing on spelling, grammar, and morphology. It aims to equip students with the skills necessary for academic and professional writing, while introducing selected examples of Arabic poetry from the pre-Islamic, Islamic, Umayyad, Abbasid, and modern periods, including free verse poetry.

‏This course provides students with the essential fundamentals of the English language, focusing on developing pronunciation and speaking skills to support academic and professional writing. Topics include demonstrative, quantifying, and interrogative determiners, nouns and their types, pronouns, adjectives and their order, subject-verb agreement, transitive and intransitive verbs, adverbs, prepositions, conjunctions, as well as reading comprehension and writing skills.

‏This course aims to study the fundamental concepts of probability theory, random variables, and discrete and continuous probability distributions. It covers common probability distributions and their applications, sampling distributions, and the foundations of statistical inference, including estimation and hypothesis testing, enabling students to analyze random phenomena and make statistical decisions.

‏This course introduces students to operating systems and their basic functions, as well as practical skills for using office applications such as Microsoft Office, including word processing, spreadsheets, and presentation software. The course also covers practical use of the Internet and email, including sending and receiving messages and using search engines.

‏This course covers the study of ordered pairs in the Cartesian coordinate system and their conversion to polar coordinates. It also explores lines and conic sections, including circles, parabolas, ellipses, and hyperbolas. The course includes vector operations and analysis of geometric relationships in the plane.

‏This course covers real functions, including exponential, logarithmic, trigonometric and inverse trigonometric functions, hyperbolic and inverse hyperbolic functions, and their derivatives. It introduces indefinite and definite integrals, integration techniques such as substitution, integration by parts, partial fractions, and trigonometric substitutions, as well as applications of integration in areas, volumes, arc lengths, and surface areas.

‏This course aims to develop students’ proficiency in Standard Arabic and equip them with the skills necessary for academic and professional writing. It covers advanced grammar topics, including subject and predicate, object of cause, object with, adverbs, interrogative structures, and demonstrative pronouns. The course also introduces students to Andalusian and modern Arabic poetry, while exploring rhetorical devices such as metaphor, simile, and metonymy.

‏This course aims to enhance students’ English skills following English Language 1. It focuses on developing reading and writing skills, expanding vocabulary, understanding dictionary entries, and covering grammar aspects such as tenses, articles, ability, permission, and necessity. Students will also practice making requests, suggestions, offers, and invitations in English.

‏This course aims to provide students with the theoretical foundations of mathematical statistics through the study of basic concepts, definitions, and theorems. It focuses on random variables, probability distribution functions, expectations and moments, selected statistical inequalities, as well as common discrete and continuous probability distributions, enhancing the mathematical understanding of probabilistic models.

‏This course aims to introduce students to the fundamental concepts of statistical sampling and survey design. It covers different sampling methods and their properties, questionnaire design and applications, estimation of population parameters, accuracy and bias of estimators, determination of optimal sample size, with emphasis on social and economic survey applications.

‏This course aims to provide students with the theoretical and applied foundations of analysis of variance and hypothesis testing for comparing means. It covers one-way, two-way, and multi-way ANOVA models, fixed, random, and mixed effects models, as well as covariance, interaction, and nested classifications, preparing students for advanced statistics courses that rely heavily on ANOVA.

‏This course aims to develop students’ basic skills in using statistical software, with emphasis on the R package, for data entry, management, and analysis. It covers descriptive statistics, graphical presentation, correlation analysis, simple linear regression, probability distributions, hypothesis testing, and one-way analysis of variance using statistical software.

‏This course covers functions of two or more variables, limits, and continuity, as well as first- and higher-order partial derivatives. It includes implicit differentiation and its geometric applications, such as gradients, directional derivatives, and tangent planes. The course also addresses maxima, minima, saddle points using Lagrange conditions, and double and triple integrals, with applications to coordinate transformations in Cartesian, polar, cylindrical, and spherical systems.

‏This course covers matrices, their types, and associated algebraic operations, as well as methods for solving various systems of linear equations. The course also addresses eigenvalues and eigenvectors, vector spaces, linear independence and dependence, and inner product spaces, with emphasis on practical applications in linear equations and linear transformations.

‏This course aims to deepen students’ understanding of mathematical statistics and develop their analytical skills in preparation for advanced statistics courses. It covers random vectors, joint and conditional probability distributions, independence, covariance and correlation, conditional expectation and variance, as well as transformation methods, cumulative distribution functions, and moment generating functions, strengthening the mathematical foundations of probabilistic models.

This course enables students to apply statistical methods in quality management, including the use, design, and analysis of control charts using statistical software such as Minitab. The course covers quality concepts, statistical measures, graphical presentation, control charts for variables and attributes, acceptance sampling, and process capability, with an emphasis on practical applications.

‏This course aims to develop students’ understanding of regression concepts and analysis, preparing them for advanced courses that rely on linear regression. It covers simple and multiple linear regression, parameter estimation and hypothesis testing, residual analysis and model diagnostics, influence analysis, variable selection methods and model building, as well as polynomial and nonlinear regression models.

‏This course covers sequences and infinite numerical series, including arithmetic and geometric sequences, positive and alternating series, with emphasis on convergence and divergence tests. It also includes power series, Taylor and Maclaurin series, and representing real functions using these series. Additionally, the course addresses improper integrals, methods for evaluating them, their properties, and convergence tests.

‏This course covers methods for solving ordinary differential equations, starting with first-order equations such as separable, linear, homogeneous, and non-homogeneous forms. It also addresses higher-order linear differential equations with constant coefficients, linear independence of solutions, existence and uniqueness theorems, and the use of differential operators to find particular solutions for non-homogeneous equations.

‏This course focuses on the properties of continuous sampling distributions and convergence theorems, enhancing students’ ability to use mathematical methods to relate different probability distributions. Topics include fundamental concepts such as beta and gamma functions, sampling distributions, the central limit theorem, the weak law of large numbers, derivation of t, chi-square, and F distributions, confidence intervals, hypothesis testing, and order statistics including median, quartiles, and range.

‏This course introduces students to fundamental concepts and techniques in nonparametric statistical analysis, highlighting the differences from parametric methods. Topics include the binomial test, sign test, Wilcoxon tests, chi-square test, McNemar test, Mann–Whitney test, Kruskal–Wallis test, Friedman analysis, Cochran’s test, and practical applications of these nonparametric tests.

‏This course provides students with fundamental knowledge of experimental design and guidance on selecting the appropriate design for different types of experiments. Topics include analysis of variance, completely randomized designs, factorial experiments with two or three factors, Latin square and replicated Latin squares, incomplete block designs, missing value estimation, and analysis of mixed experiments.

‏This course covers complex numbers and analytic functions, including their polar representation, roots and powers, and De Moivre’s theorem. It focuses on analytic functions and the Cauchy-Riemann equations, elementary functions (exponential, logarithmic, trigonometric), and complex integrals, including path integrals, Cauchy’s theorem, and its applications. The course also addresses sequences and series, power series such as Taylor and Laurent series, residues, classification of singular points, with applications in transformations and mappings.

‏This course introduces the fundamentals of numerical analysis, including the study of errors and their types and calculation. It covers numerical methods for solving nonlinear and linear equations, including direct and iterative methods, polynomial interpolation, and numerical differentiation and integration. The course also addresses numerical solutions of ordinary differential equations and eigenvalue problems, with emphasis on accuracy and error analysis of the applied methods.

‏This course aims to enable students to understand point and interval estimation, the properties of good estimators, and methods for obtaining them. Topics include different estimation methods such as the method of moments, maximum likelihood, sufficient statistics and minimal sufficiency, unbiased estimators, minimum variance, confidence intervals using classical and Bayesian approaches, and estimation of differences between means and proportions.

‏This course provides an in-depth study of moments, moment-generating functions, and random variables, as well as characteristic functions and their properties. It covers probability convergence and its types, order statistics concepts, the law of large numbers, the central limit theorem, and convergence of random sequences.

‏This course introduces students to various sampling methods, including determining sample size and estimating the population mean, total, and variance. Topics cover probabilistic samples, including simple random, stratified, systematic, and cluster sampling, as well as non-probabilistic samples such as quota and judgment samples, with emphasis on applications and optimal sample size selection.

‏This course introduces students to fundamental skills in using statistical software packages such as Minitab, SPSS, and Stata, applying them to perform appropriate statistical analyses for scientific problems, interpreting results, and preparing statistical reports. Topics include descriptive statistics, graphical representation, parametric and nonparametric tests, correlation and regression analysis, time series analysis, matrices, simulation, and goodness-of-fit tests for probability distributions.

‏This course focuses on methods for analyzing linear and nonlinear time series and understanding statistical models representing their behavior. Topics include time series components and presentation, trend and seasonal analysis, moving averages, autoregressive and moving average models, cyclical and irregular variations, exponential smoothing, nonlinear models, and the study of indices and their statistical properties.

‏This course introduces students to the fundamentals of scientific research and academic writing skills. Topics include the concept and importance of research, characteristics of good research, designing a research plan and data collection, reviewing previous works, methods for searching literature, skills for writing the theoretical part of the research, references, abstract, and final preparation of the research report.

‏This course provides students with a comprehensive understanding of statistical hypotheses, how to test them, types of errors, the power function, most powerful tests, and uniformly most powerful tests. Topics include tests on means and variances, chi-square tests, sequential hypothesis testing, Neyman-Pearson theory, and approximate sample size determination.

‏This course introduces students to the fundamentals of linear algebra, including key concepts, definitions, and theorems, preparing them for advanced courses that rely on linear models. Topics include matrix algebra review, solving linear equations, quadratic forms, matrix differentiation and integration, multivariate normal distribution, marginal distributions, regression and correlation, noncentral chi-square and F distributions, quadratic form predictions, and simple linear regression.

This course introduces the fundamentals of operations research, focusing on linear programming. Topics include linear models, objective functions, constraints, decision variables, dummy variables, graphical and simplex solution methods, duality and the corresponding program, sensitivity analysis, integer programming, transportation and assignment problems, and decision theory.

‏This course focuses on modern time series models and their analysis, providing an overview of the Box-Jenkins methodology. Topics include stationarity and its tests, autocorrelation and partial autocorrelation functions, autoregressive and moving average models, mixed models, ARMA, ARIMA, SARIMA models, and modern forecasting methods.

‏This course studies stochastic processes and Markov chains, focusing on steady-state analysis, transition matrices, absorption probabilities, Poisson processes, and birth-death chains. The course includes various exercises and applications to understand the properties of stochastic processes.

‏This course covers multivariate statistical methods and their practical applications using appropriate statistical software. Topics include the multivariate normal distribution, inference on mean vectors, confidence regions, multivariate analysis of variance, principal component analysis, discrimination and classification, and cluster analysis, with practical implementation in R-package.

‏This course introduces the fundamental theories of the general linear model and enables students to apply related methods such as the square root method, point estimation, and analysis of variance and covariance. The course also covers multiple regression and correlation applications, as well as experimental designs with one and two factors. It prepares students for advanced courses that rely on linear models.

This course covers the study of data theory and its practical applications, including optimal tree theory, shortest path theory, and maximum flow theory. It also addresses business network analysis using the critical path method, program evaluation techniques, optimal inventory management, Markov models and matching theory, simulation, and queueing systems, with emphasis on practical applications.

The Graduation Project course is one of the core courses in the Department of Statistics program. It aims to enable students to apply theoretical knowledge and practical skills acquired during their studies within an integrated research framework. The course focuses on developing the student's ability to design statistical studies, collect and analyze data using appropriate statistical methods, and interpret scientific results in a systematic and accurate manner. The course also provides students with the opportunity to develop practical skills such as using statistical software, preparing research reports, working in research teams, and connecting academic research to practical and societal issues, thereby preparing students for the labor market and active participation in research projects.