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, collaborative tutorials, and practical lab sessions to cultivate data analytics proficiency. Lectures establish foundational concepts through case studies and theoretical discussions, while tutorials deepen understanding via problem-solving activities and peer dialogue. Lab sessions prioritize hands-on experimentation with analytical tools, enabling students to apply techniques to real-world datasets. Structured independent study reinforces learning through guided exercises, supplementary readings, and exploratory coding tasks. Students receive personalized feedback during tutorials to refine technical and analytical approaches, fostering critical thinking and adaptability. Collaborative group tasks in labs simulate industry workflows, bridging theory and practice. This blended approach supports diverse learning styles, encouraging continuous engagement with module content while nurturing problem-solving autonomy and technical expertise.