MATH42515: Data Exploration, Visualization, and Unsupervised Learning

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• To introduce the concepts and methods of exploratory data analysis, data visualization, and unsupervised learning

• Advanced exploratory data analysis
• Density estimation and data visualization
• Unsupervised learning and clustering
• Principal component analysis (PCA) and dimension reduction
• Data visualization and statistical computing with R
• Methods for non-numerical data: e.g. categorical, spatial and temporal, text, images, networks, graphs.
• Further topics: e.g. anomaly detection, treatment of missing values, association rules.

Subject-specific Knowledge:

• By the end of the module students will:
• Have a systematic and coherent understanding of the theory, computation, and application of the topics studied.
• Have mastered advanced exploratory data analysis, data visualization, and statistical computing with R.
• Have acquired a coherent body of applicable knowledge on density estimation, unsupervised learning, clustering, PCA and dimension reduction.

Subject-specific Skills:

• In addition, students will be acquired:
• Ability to use statistical software R to conduct synthesis of data, data analysis, and date visualization.
• Programming skills generally used in advanced methods such as clustering and unsupervised learning.
• Ability to identify and apply appropriate unsupervised learning methods to modern real-world problems.

Key Skills:

• Sufficient mastery of advanced data analysis, data visualization and unsupervised learning methods and ability to apply them appropriately to real-world applications.
• Ability to clearly communicate statistical methods and relevant conclusions through writing.
• Ability to organise prioritise, and manage time effectively.
• Ability to advance and extend their knowledge through significant independent learning and research.

• This module will be delivered by the Department of Mathematical Sciences.
• Teaching will be delivered primarily by workshops and lectures.
• Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
• Workshops describe theory and its application to concrete examples, enable students to test and develop their understanding of the material by applying it to practical problems, and provide feedback and encourage active engagement.
• Workshops are a combination of live lectures, computer practicals, problem classes, tutorials and guided group work.
• Lectures and workshops will be supported by the distribution of materials such as video content, directed reading, e-assessments, reflective activities, opportunities for self-assessment, and peer-to-peer learning within a tutor-facilitated discussion board.
• Students will be able to obtain further help in their studies via scheduled office hours or surgeries as well as by approaching their lecturers by email.
• Students will be expected to work in between workshops and lectures, and to discuss their own work during the workshops. This work will be guided by the module leader, but will be organised by the students themselves, thereby enabling them to demonstrate their time management skills.
• Students will undertake independent research to further their knowledge of the topic and self-directed learning to further their technical and transferable skills.
• The workshops also provide opportunities for module leaders to monitor progress and to provide feedback and guidance on the development of ideas for the project, and for students to gauge their progress throughout the duration of the module.
• Student performance will be assessed through two individual assignments and four quizzes (e-assessments).
• The quizzes (e-assessments) enable the students to put into practice learning from lectures and strengthen their understanding.
• The assignments will provide the means for students to demonstrate their acquisition of subject knowledge and the development of their problem-solving skills.

Workshop discussion of students' ideas and experiences; informal discussions of student progress with module leader when necessary; interim feedback via continuous assessments.

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