2025 - 2026

This course introduces students the fundamental concepts of probability theory. Topics covered include: sample space, events, conditional probability,
independence, Bayes’ formula, discrete random variables, continuous random variables, distribution functions, expected values, variance, expectation and variance of a function of a random variable, binomial distribution, negative binomial distribution, Poisson distribution, geometric distribution, hypergeometric distribution, uniform distribution, normal distribution, exponential distribution, gamma distribution, jointly distributed random variables, covariance, Chebyshev’s inequality, central limit theorem, law of large numbers.

In this course, we disucss about the basics of algebra and analytic geometry.

This course covers various topics on probability and statistics, including conditional probability, random variables and distributions, expectation, typical distributions, stochastic processes, estimation, testing hypotheses, categorical data and nonparametric methods, and linear statistical models.

This course introduces students to basic probability theory and some probabilistic models.

This course covers various topics on probability and statistics, including conditional probability, random variables and distributions, expectation, typical distributions, stochastic processes, estimation, testing hypotheses, categorical data and nonparametric methods, and linear statistical models.

This course introduces students to key concepts in optimization, with a focus on their practical applications in business and e-commerce. The topics include matrix
algebra, linear programming techniques, graphical solution methods, and sensitivity analysis. The course also explores specialized applications such as marketing
optimization, financial planning, operations management and network distribution models. Students will learn to model, analyze, and solve real-world business
optimization problems. The course demonstrates how mathematical tools can support strategic decision-making, improve operational efficiency, and enhance resource
management in data-driven business environments.

This course introduces students to single-variable calculus. Topics covered include: functions, limits, continuity, derivatives, the mean value theorem for derivatives, applications of derivatives, rates of change, extrema of functions, definte integrals, indefinite integrals, the fundamental theorem of calculus, the mean value theorem
for integrals, approximations of integrals, applications of integrals, techniques of integration, and improper integrals.

This course introduces students to basics techniques in solving ordinary differential equations. Topics covered include: direction fields, first order linear equations, separable equations, exact equations, integrating factors, existence and uniqueness theorem, second order linear equations, homogeneous equations, inhomogeneous equations, method of underdetermined coefficients, variations of parameters, higher order linear equations, series solutions, Laplace transforms, Laplace transform methods in solving ordinary differential equations, systems of first order linear equations.

This course introduces students to the methods for analyzing time series. Topics covered include: time series, forecasting, stationary processes, AR. MA, ARMA, ARIMA, ARCH, GARCH models, spectral analysis, model fitting, long memory processes.

This course introduces students to the methods in statistics required for data analysis. Topics covered include: descriptive statistics, exploratory data analysis, sampling methods, point estimation, confidence intervals, hypothesis testing, chi-squared tests, single factor analysis of variance, simple linear regression, and 
some distribution free procedures.