COURSE CURRICULUM

The Data Science and Business Analytics program equips students with the technical knowledge and practical skills needed to analyse and interpret complex datasets for business applications. In the first semester, students study Data Analytics Infrastructure and Big Data Applications, which provide a foundation in business intelligence, analytics, and data infrastructure, alongside practical experience with programming and large-scale data systems. The Data Analytics Infrastructure module covers the concept of business intelligence systems, their architecture, business metrics, and the alignment of data strategies with organisational goals, while also addressing the design of data warehouses using relational databases and dimensional modelling. The Big Data Applications module focuses on advanced computational tools, including the Hadoop Distributed File System and Apache Spark, demonstrating data mining, machine learning algorithms, real-time analytics, graph processing, sentiment analysis, and timeseries analysis using industry-standard software such as SAS Enterprise Guide. In the second semester, the Data Mining Techniques and Methods module allows students to review and apply various methods for extracting meaningful insights from large datasets, using tools such as SAS Enterprise Miner, Weka, or R, while discussing current application areas and research developments. The Project Proposal, Planning and Project Management module develops students’ research and statistical skills, including probability, distributions, parametric and non-parametric analysis, sampling, and critical evaluation of results, preparing them for their individual research projects. Blocks 5 and 6 focus on the PGT Project, a self-managed, in-depth study where students integrate the skills and knowledge gained across the program to address complex problems or research questions, often involving incomplete or contradictory data. Teaching is delivered through lectures, seminars, workshops, and tutorials, with assessments comprising individual and group work, presentations, reports, projects, and exams. The block teaching approach allows students to focus on one subject at a time, promoting deeper engagement and practical application, ensuring graduates develop the analytical, technical, and research expertise required for careers in data science, analytics, and business intelligence.