| US Credits | UK Credits |
| From this list you must take one Level 4 course and two courses from Level 5/6 | | |
- MATH 4101 Probability and Statistics
This course in probability and statistics includes theoretical and applied approaches which are primarily designed for business, data science, social science and psychology majors. The course coverage will include: descriptive statistics, elementary probability theory, random variables and expectations, discrete probability distributions (Binomial and Poisson distributions), continuous probability distribution (Normal distribution), sampling distributions, estimation and hypothesis testing about the mean and proportions of up to two populations, Chi-square tests, One-way ANOVA and F Distribution, linear, multiple and non-linear regression and N on-parametric methods . SPSS lab sessions will be included targeting applications of statistical concepts to business, data science, social science and psychology. All practical work will be produced using SPSS statistical software. | 4 | 16 |
- MATH 4102 Mathematics of Argument and Reasoning
This course presents the mathematics of arguments and reasoning by introducing students to concepts in logic and discrete mathematics. It examines the nature of logic, propositional and deductive logic, tautologies and contradictions, algebra of sets, relations, Boolean functions, and graph theory. The topics covered will include propositional calculus, methods of deduction, and quantification theory, leading to an introduction to first order logic, proof by induction and recursive relations. Valid and invalid argument forms and their tests will be performed. Matrix algebra and determinants and their applications in solving systems of equations will be covered. Applications of these concepts to logical networks, switching circuits and network analysis will be investigated. | 4 | 16 |
- MATH 4103 Calculus with Applications
This course provides a sound understanding of the concepts of calculus and their applications to business courses, data science and scientific courses. There will be an emphasis on the rigorous approach to the concept of limits as a key pillar of many concepts of calculus. Theory and applications of calculus with numerous examples will be explained to students depending on specific majors . Topics to be covered will include co- ordinate geometry of straight lines, quadratic curves, exponential and logarithmic functions; elementary differentiation and integration of real-valued functions, exponential and logarithmic functions, and trigonometric functions. Applications of calculus concepts to maxima, minima, and optimization problems. Calculus of several variables to include partial derivatives and optimizing functions of two variables, and constrained optimization and method of Lagrange Multipliers. Double integrals and applications. Modelling with first-order ordinary differential equations. | 4 | 16 |
- MATH 5103 Game Theory and Decision Methods
This course introduces game theory and covers the core principles of game theory and its role in the process of strategic decision-making methods in business, economics, and social sciences. The use of game algebra and the analysis of the structure of various types of practical decision problems as applied especially to business environments will be emphasized. The areas to be studied will include strategic decision making under uncertainty for both non-probabilistic and probabilistic scenarios, risk analysis, Bayes’ Theorem, decision trees, systems of linear equations and basic matrix methods, linear programming, Markov Processes, game strategies, game trees, the Nash equilibrium, and classification of games as sequential-move games and simultaneous-move games to include mixed strategy games, the prisoner’s dilemma, zero-sum and two-persons games, and hawk-dove games. Detailed applications to specific strategic situations such as in bargaining, bidding and market competition will be explored. | 4 | 16 |
- MATH 5104 Linear Algebra and Multivariable Calculus
The course provides a detailed study of set theory, systems of linear equations, theory of vectors and vector spaces, algebra of matrices, determinants and characteristic polynomials, mappings and linear transformations, canonical forms and invariance, eigenvectors and eigenvalues. LU-matrix factorization, matrix diagonalization and orthogonality, least square method, and inner-product spaces and applications. Multivariable calculus to include solutions of systems of linear ordinary differential equations (ODE) using matrix method. These concepts are useful as they form a basis of a deeper understanding of advanced mathematics and have wide applications in physical and social sciences. Applications to specific majors will be explored. | 4 | 16 |
- DATA 5102 Data Analytics and Visualization
This course aims to equip students with a comprehensive understanding of fundamental concepts in data analysis and visualization with an exploration of big data. The primary focus will be on utilizing Tableau as a powerful tool for data visualization while also introducing students to Python and R for data processing and analysis. Through hands-on practice and theoretical learning, students will develop the skills necessary to analyse and visualize data effectively. Additionally, the module will delve into the complexities of big data, providing insights into its management, processing, and the challenges associated with its analysis. By the end of this module, students will have a strong understanding of using Tableau for visual representation and gain introductory knowledge of Python’s and R capabilities in handling and analysing data based on descriptive, diagnostic, predictive and prescriptive analytics, which will enable learners to create meaningful insights from diverse datasets. | 4 | 16 |
- MATH 6101 Advanced Computational Methods in Data Science
This course provides a deep dive into advanced computational techniques used in data science. It is designed to equip students with a solid foundation in the computational methods necessary for processing and analysing large-scale datasets. Students will learn about high-performance computing, optimization algorithms, advanced numerical methods, and specialized techniques for data analysis, visualization, and interpretation. Emphasis will be placed on both theoretical understanding and practical implementation of computational algorithms, including parallel computing, optimization strategies, and their applications in big data analytics and complex problem-solving. | 4 | 16 |
- MATH 6102 Machine Learning and Predictive Analytics
This course provides an in-depth understanding of Machine Learning (ML) and Predictive Analytics, focusing on algorithms, methodologies, and applications. Students will explore fundamental ML models, from supervised and unsupervised learning to more advanc ed topics such as deep learning and ensemble methods. The course emphasizes both theoretical understanding and practical implementation using real-world datasets. Students will be taught to use ML to build predictive models, evaluate their performance, and apply them to solve complex problems in various fields, including finance, healthcare, and technology. | 4 | 16 |
- MATH 6103 Time Series Analysis and Forecasting
This course introduces students to the fundamentals and advanced techniques of time series analysis and forecasting. Students will learn how to model, analyze, and forecast time-dependent data using both statistical and machine learning approaches. The course will cover classical time series models such as ARIMA, exponential smoothing, and state- space models, as well as more advanced techniques including neural networks and Long Short-Term Memory (LSTM) models. The emphasis will be on practical applications in various fields, including finance, economics, and environmental sciences, using real-world datasets and computational tools like Python and R. | 4 | 16 |
- ECON 6101 Econometrics
This course bridges the gap between econometric theory and statistics and applied software techniques. Provides the necessary background to assess economic decisions and run linear regression, understanding the origins of the technique. Moreover, this course has an applied course dimension in modelling data via software applications, particularly time series data, as a practical guide to quantitative research in Economics and Finance. | 4 | 16 |
| Minor Requirements | 12 | 48 |
