Master of Science in Applied Data Science

This course explores advanced data analytics techniques, focusing on data mining methodologies and their application to large datasets. Students will learn about association rule mining, clustering methods like k-means and hierarchical clustering, and advanced classification techniques such as support vector machines and ensemble learning. The course also introduces data mining techniques on text and graph, enabling students to uncover patterns and insights from complex data. The course will also introduce data analytics applications in various applications, e.g., education, healthcare, finance. Practical sessions emphasise hands-on experience with data mining tools and software, preparing students to analyse and interpret data in various domains.

This course aims to provide students with the opportunity to renew and expand their understanding of Python programming through practical coding exercises and projects. In this course, students will enhance their programming skills by learning advanced programming techniques such as functional, logical, and concurrent programming. The course emphasises practical applications and encourages creativity and problem-solving. Students will deepen their understanding of advanced data flow and control flow. Additionally, this course will introduce students to the emerging field of generative AI and its potential to assist in programming tasks. Students will learn how to harness generative AI to seek insights for programming tasks, generate code snippets, and enhance code quality through modern code review.

This course provides an in-depth exploration of database systems and management practices. Students will learn about database design principles, relational algebra, SQL for data manipulation, and data warehouse. The course covers both relational databases and NoSQL systems, emphasising their use in managing large-scale big data. Topics include transaction management and concurrency control, data integrity, and performance optimisation. Students will gain hands-on experience in designing and implementing database solutions, essential for effective big data management in diverse application scenarios.

This course provides a comprehensive introduction to the foundational concepts and methodologies in data science and artificial intelligence, under the SEMMA (Sample, Explore, Modify, Model, Assess) framework developed by SAS and the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework developed by IBM and other companies respectively. Students will learn the foundations for data collection, preprocessing, exploratory data analysis, data visualisation, along with an introduction to descriptive and inferential statistical methods, and supported by statistical packages such as Jamovi or R/Python as tools for data analysis. The course also covers fundamental machine learning algorithms such as linear regression, logistic regression, decision trees, and clustering techniques, as well as basic AI concepts including neural networks and search algorithms. Ethical considerations and the societal impact of AI and data science are discussed to provide a holistic understanding of the field. This foundational knowledge prepares students for advanced coursework and practical applications in data science and AI.

This course provides students with an in-depth understanding of the principles and theories of artificial intelligence models and algorithms, covering advanced topics including deep learning, computer vision, and natural language processing (NLP). Students will explore the theoretical underpinnings and practical implementations of neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Additional topics include reinforcement learning, generative adversarial networks (GANs), and the development of AI systems for real-world applications. The course also examines ethical considerations and the development of trustworthy AI systems, preparing students to innovate responsibly in the field of AI.

This course begins by reviewing basic and essential knowledge and skills in cyber security and problem solving for Education. This course will explore key cyber security concepts such as cryptographic hash function, digital signature, authentication, public key infrastructure, firewall, intrusion detection, access control, and relevant applications in cyber security education. The course aims to provide students with overall understanding of cyber security landscape. Also, participants will learn about how to solve practical cyber security issues and design solutions for cyber security education.

This course introduces students to data visualisation techniques, focusing on learning Python packages for creating 2D and 3D visualisations. Students will learn about libraries such as Matplotlib, Seaborn, and Plotly, and how to use them to create informative and interactive visualisations. The course also covers dimension reduction techniques and the principles of human-computer interaction to enhance the usability and interpretation of visual data. Through hands-on exercises, students will develop the skills needed to effectively communicate data insights through visual storytelling.

This course focuses on predictive analytics techniques and their application to decision-making processes. Students will explore methodologies such as time series forecasting and regression models. The course emphasises the use of predictive models to anticipate future trends and outcomes, including estimating future values of a specific variable (e.g., sales, demand, temperature) and predict outcomes or behaviours (e.g., customer churn, fraud risk) to support strategic planning and decision-making. Through practical exercises and projects, students will learn to apply predictive analytics to transform data into actionable insights, aligning with the programme’s goal of creating innovative data-driven solutions.

This course focuses on generative AI models and their applications across various domains. Students will explore concepts such as generative adversarial networks (GANs), variational autoencoders, and large language models like GPT. The course emphasises the development and implementation of generative AI systems for tasks such as image synthesis, text generation, and creative content creation. Students will gain hands-on experience with generative AI frameworks and tools, preparing them to innovate and develop applications that leverage generative AI techniques. 

This course examines the application of learning analytics techniques to education settings to enhance learning outcomes and educational practices. Students will learn about learning analytics, learning behaviour modelling, and practical data mining and machine learning techniques such as classification, clustering, and association rule mining in educational contexts. Ethical considerations in analysing student data will also be discussed. The course focuses on analysing student data to identify patterns and predict learning outcomes, supporting personalised learning and informed decision-making in education. By integrating theoretical concepts with practical applications, students will be equipped to transform educational data into actionable insights.

This course examines the intersection of social computing and sentiment analysis, focusing on techniques for analysing social media, social networks, user interactions, and online behaviour. Students will learn about text mining, graph mining, and sentiment analysis methods to extract insights from social data. Practical exercises and projects will enable students to apply these techniques to real-world social computing challenges, transforming data into actionable intelligence.