The Mathematics and Statistics discipline offers a rigorous and structured foundation in quantitative reasoning, logical thinking, and analytical problem-solving. The curriculum spans core areas such as calculus, real analysis, linear algebra, discrete mathematics, probability, statistical inference, operations research, and computational methods. Students engage with both pure and applied perspectives, learning to model real-world problems, analyze data, and interpret results across disciplines such as economics, management, social sciences, and technology. Emphasis on proofs, modelling, optimization, and software-based analysis is reinforced through tutorials, projects, and continuous assessment. The programme equips learners with strong quantitative skills for research, analytics, finance, data science, and advanced academic pursuits.
Foundation Course in Mathematics and Statistics equips students with essential quantitative skills required for management studies and analytical decision-making. The course introduces fundamental concepts in set theory, logic, functions, matrices, permutations and combinations, and basic statistics. Emphasis is placed on understanding mathematical reasoning, problem-solving techniques, and statistical measures used to describe and interpret data. Through a structured progression from theory to application, students learn to analyze rates of change, matrix operations, and data distributions. Lectures are supported by tutorials that focus on hands-on practice, enabling learners to build confidence and competence in applying mathematical and statistical principles to real-world business contexts.
Calculus introduces students to the mathematical language of change, accumulation, and functional relationships. The course covers foundational concepts including variables, functions, limits, continuity, differential and integral calculus, and sequences and series. Emphasis is placed on developing conceptual understanding alongside computational skills, with applications spanning optimization, rate of change, area calculations, and physical interpretations. Students engage in problem-solving, curve sketching, and series analysis, building geometric and graphical intuition. Through interactive lectures, computational exercises, and group discussions, learners develop the ability to model and analyze real-world problems, bridging mathematical theory with practical applications across diverse disciplines.
Statistical Methods I introduces students to the foundational concepts of probability and statistical analysis essential for modeling and interpreting real-world data. The course covers probability theory, random variables, key probability distributions, correlation, and regression techniques, including multivariate analysis. Emphasis is placed on both conceptual understanding and practical application through data analysis, visualization, and the use of statistical software. Interactive lectures, problem-solving sessions, and computational projects enable students to develop analytical reasoning, interpret statistical relationships, and communicate results effectively. By the end of the course, students are equipped to construct, analyze, and evaluate statistical models across disciplines.
The course Statistical Methods II builds advanced statistical skills, focusing on classical inference, hypothesis testing, and estimation techniques. Students learn to design and implement statistical analyses, interpret models, and assess their validity using probability and statistical tools. Key topics include sampling methods, t-tests, chi-square tests, F-tests, correlation, regression, and analysis of variance (ANOVA), with applications in experimental design. The course combines lectures, interactive discussions, computational projects, and software-based implementations to develop analytical and problem-solving skills. By the end, students can formulate research hypotheses, apply appropriate statistical methods, and effectively communicate their findings in verbal, visual, and written forms.
Linear Algebra builds a strong foundation in vectors, matrices, and linear systems essential for quantitative analysis and decision-making. The course covers vector spaces, matrix operations, eigenvalues, eigenvectors, and linear transformations, with emphasis on geometric interpretation and real-world applications. Students learn to solve systems of linear equations using multiple methods and analyze matrix properties such as rank and subspaces. Through interactive lectures, problem-solving sessions, computational projects, and presentations, the course develops logical reasoning and the ability to model practical problems using linear algebra concepts.
Graph Theory introduces students to the mathematical foundations of graphs and their wide-ranging applications in real-world networks. The course develops core concepts such as vertices, edges, connectivity, trees, directed graphs, and network flows, along with key algorithms including Dijkstra’s, Kruskal’s, and Ford–Fulkerson. Moving beyond classical theory, it explores modern network science topics like random graphs, scale-free and small-world networks, robustness, communities, and spreading phenomena. Emphasis is placed on constructing and analyzing networks arising in management, analytics, and social systems. Through interactive lectures, computational projects, and problem-solving sessions, students gain both theoretical understanding and practical analytical skills.
This course in Real Analysis introduces students to the rigorous study of real numbers, sequences, series, and functions, emphasizing formal proofs and abstract reasoning. Topics include metric spaces, limits, continuity, differentiation, and Riemann integration, with applications to practical problems. Students develop the ability to construct proofs, understand convergence, and apply analytical concepts in sequences, series, and functions. Pedagogy combines lectures, interactive discussions, computational exercises, and group problem-solving, fostering deep conceptual understanding. Assessments include written exams, presentations, reading assignments, and projects, ensuring mastery of both theoretical foundations and real-world applications of real analysis in mathematics and related disciplines.
The Theory of Distributions course introduces students to advanced concepts in probability distributions, covering both discrete and continuous random variables, including uniform, Bernoulli, binomial, geometric, Poisson, exponential, normal, and Pareto distributions. Students learn key properties such as mean, variance, and probability density functions, along with applications in data analysis and simulation. The course also explores parametric simulation techniques, pseudo-random number generation, and sampling methods. Pedagogy combines lectures, interactive discussions, computational exercises, and problem-solving sessions. Assessments include exams, presentations, projects, and literature reviews, equipping students with rigorous theoretical understanding and practical skills in statistical modeling and simulation.
The Group Theory course provides an in-depth study of algebraic structures, focusing on groups, subgroups, cosets, permutation groups, normal subgroups, homomorphisms, and isomorphisms. Students explore group actions in algebraic and geometric contexts, learning to define groups via generators and relations and apply key theorems such as Lagrange’s, Cayley’s, and Cauchy’s theorems. The course emphasizes abstract reasoning, problem-solving, and logical derivation of properties, including applications to quotient groups and permutation structures. Pedagogy combines lectures, interactive discussions, computational exercises, and group problem-solving. Assessment includes exams, projects, presentations, and weekly problem sets, equipping students with rigorous theoretical knowledge and practical analytical skills.
The Operations Research course equips students with the skills to model, analyze, and solve real-world decision-making problems using linear programming and related techniques. Students learn to formulate problems, apply graphical and simplex methods, understand duality, and address special cases such as transportation problems. The course also covers network analysis through CPM and PERT, and introduces game theory with strategic problem-solving. Pedagogy emphasizes interactive lectures, computational implementations, active learning, and group problem-solving. Assessments include examinations, presentations, and discussions. By integrating theory with practical applications, the course develops analytical, modeling, and critical-thinking skills essential for optimizing complex systems.
The Concepts in Pure Mathematics course introduces students to foundational topics in discrete mathematics, number theory, and complex numbers. Key areas include set theory, equivalence relations, counting principles, functions, induction, recurrence relations, divisibility, GCD and LCM, congruences, and complex number operations with geometric interpretations. Students learn to apply these concepts to develop rigorous mathematical reasoning and prepare for advanced mathematical topics. Pedagogy combines interactive lectures, computational exercises, problem-solving sessions, and group discussions. Assessment includes exams, presentations, projects, and weekly problem sets, ensuring students build both theoretical understanding and practical skills in mathematical analysis and discrete problem-solving.
The Advanced Calculus course develops a deep understanding of calculus concepts with applications in modeling and problem-solving. It covers single-variable calculus review, ordinary differential equations (ODEs), partial derivatives, multiple integrals, and partial differential equations (PDEs), emphasizing graphical, numeric, symbolic, and descriptive approaches. Students learn to analyze and solve real-world problems, from mechanical oscillations to heat transfer, using advanced calculus techniques. Pedagogy combines interactive lectures, computational exercises, active learning, and group problem-solving. Evaluation includes exams, presentations, and discussions. By the end, students gain strong analytical skills and practical competence in applying advanced calculus to scientific, engineering, and mathematical contexts.
The Mathematics for Machine Learning course equips students with essential mathematical foundations for machine learning, including linear algebra, multivariable calculus, probability, and statistics. It covers optimization methods, linear and logistic regression, supervised and unsupervised learning, and neural networks. Students learn to analyze and interpret machine learning models, apply gradient-based optimization, and implement algorithms using programming tools like Python. Pedagogy combines interactive lectures, collaborative problem-solving, hands-on exercises, assignments, and projects. By the end of the course, students gain the mathematical and computational skills required to design, evaluate, and optimize machine learning models across real-world applications in AI and data science.
The Bayesian Inference course introduces students to the principles and applications of Bayesian statistics in real-life scenarios. It covers Bayesian and non-Bayesian inference, prior and posterior distributions, conjugate priors, and Bayes estimators under various loss functions. Students learn to determine and evaluate subjective, non-informative, and robust priors, construct conjugate prior families, and apply Bayesian interval estimation. Pedagogy combines classroom and laboratory sessions with assignments, quizzes, group discussions, presentations, and seminars to reinforce learning. By the end of the course, students gain practical skills in applying Bayesian inference to decision-making, estimation, and statistical modeling across diverse applied contexts.
