Aims and Fit of Module

This module equips students with core principles and techniques of Big Data Analytics  (BDA) to analyze large datasets. It covers big data collection, scalable algorithms, pattern discovery, and predictive analytics, fostering practical skills in preprocessing and interpreting complex data.
By integrating theory with real-world applications, the module prepares students for roles in finance, healthcare, and technology. Ethical data practices and modern analytical tools are emphasized, ensuring graduates can extract actionable insights and address evolving challenges in data science and business intelligence.

Learning outcomes

A. Analyze and critically evaluate the key concepts, processes, and issues related to Big Data Analytics (BDA);
B. Assess and formulate strategic applications of BDA to enhance and optimize business operations;
C. Design and critically evaluate advanced machine learning algorithms (i.e., ensemble methods, deep learning architectures) using industry-standard tools (i.e., scikit-learn, PySpark) to address complex, large-scale data analysis challenges.
D. Develop, deploy, and validate end-to-end data analytics solutions using specialized software packages (i.e.,  Python’s Pandas/NumPy) to solve domain-specific business problems.

Method of teaching and learning

The module integrates interactive lectures and practical lab sessions to cultivate data analytics proficiency. Lectures focus on establishing foundational theoretical frameworks, exploring case studies, and discussing the algorithmic principles of data science. These sessions provide the conceptual basis required for the data pipeline. Practical Lab Sessions  have been expanded to provide a more immersive, hands-on learning experience, replacing traditional tutorials. This intensive format (4 hours per week over 6 weeks) allows students to engage deeply with the end-to-end data process: The initial phase focuses on data acquisition (web crawling) and visualization. The subsequent phase emphasizes algorithmic application, classification modeling, and performance evaluation. During these extended labs, students receive real-time, personalized feedback on their technical implementation and analytical approaches, fostering critical thinking and problem-solving autonomy. Independent Study complements the formal contact hours. It is structured around guided exercises, exploratory coding tasks, and supplementary readings, encouraging students to refine their technical skills and prepare for assessment components.