Minor in
Data Science

US Credits

UK Credits

• 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 Non-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. 

• COMP 4101 Introduction to Programming

  ​​This course provides the fundamentals of object-oriented programming.  This will include usage of variables, objects, classes; assignment and control through statements, loops, functions, procedures, interaction between objects and inheritance. This course may introduce any current specialists programming topics, eg. programming for mobile applications. ​ 

• DATA 5102 Data Analysis and Visualisation

  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.

• DATA 5103 Database Systems

  This course explores incorporating data into web development. This course covers data modelling, data representation, along with practical components of data protection and security using industry standard query platforms such as SQL and No SQL DBMS. Students will be able apply these server-side programming skills as a response to professional briefs.

• 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.

• MATH 6102 Machine Learning and Predictive Analysis

  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 advanced 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 build predictive models, evaluate their performance, and apply them to solve complex problems in various fields, including finance, healthcare, and technology.