This module examines the limitations of traditional quantitative methods in social science, focusing on real-time analytics, dynamic data interpretation, and historical shifts in scientific approaches. Learners will explore why certain statistical concepts are overlooked and how these gaps affect practical decision-making in complex systems.

This module explores the concept of big data by examining its historical roots, theoretical frameworks, and real-world implications. Learners will analyze how big data is defined, its evolution over time, and the challenges and opportunities it presents in data-driven decision-making.

This module introduces the foundational concepts of data mining, including pattern recognition, knowledge extraction, and the challenges of ensuring model validity and reliability. It explores the historical context and conceptual framework of data mining in relation to big data. Learners will gain a clear understanding of the key principles and limitations of data mining techniques.

This module explores the intersection of complexity sciences, quantitative social science, and data mining. Learners will gain an understanding of the conceptual challenges and historical context of complexity theories. The focus is on analyzing how these ideas shape modern data analysis practices.

This module explores the concept of case-based complexity in data mining, focusing on ontological frameworks, mathematical formalisms, and techniques for grouping and predicting case profiles. Learners will gain an understanding of how to analyze similarities between data mining methods and apply these concepts using practical examples.

This module introduces key concepts in classification and clustering, focusing on how data can be organized using both supervised and unsupervised learning methods. Learners will gain insight into the practical applications of these techniques and how they support decision-making. The module also covers the foundational elements of building and evaluating classification models.

This module provides an overview of the historical development and key concepts of machine learning, including its application in big data analysis, self-organising maps, genetic algorithms, and swarm intelligence. Learners will explore how these techniques solve complex optimisation problems and understand their real-world relevance.

This module covers key techniques in predictive analytics, including Bayesian statistics, hypothesis testing, and decision trees. Learners will gain skills in applying data-driven methods to forecast outcomes in real-world scenarios like flight scheduling and revenue optimization.

This module explores advanced techniques for analyzing time-based data, focusing on predicting trends using time series, dynamic clustering, and vector field modeling. It delves into the limitations of traditional methods and introduces concepts like multiple group growth curve modeling and differential equations. Learners will gain a deeper understanding of how to model complex dynamic systems in real-time big data environments.

This module introduces geospatial modeling using GIS tools and spatial data analysis. It covers fundamental concepts, real-world applications, and analytical techniques for understanding geographic data and patterns.

This module introduces the principles and methods of complex network analysis, covering how networks are structured, modeled, and analyzed. Learners will explore real-world applications such as airline route optimization and epidemiological modeling, and gain an understanding of key network properties and computational tools. By the end, students will be equipped to analyze and interpret complex network data.

This module explores techniques for analyzing unstructured data through textual and visual data mining, focusing on identifying trends, interpreting sentiment, and presenting insights for informed decision-making in business contexts.

This module explores how complexity science can enhance digital social science research, focusing on methodological rigor, power dynamics, and the challenges of analyzing big data. It provides practical guidelines for modeling complex systems and understanding the limitations of holistic approaches. Learners will gain insights into integrating interdisciplinary methods for more effective data analysis.