This module is intended to provide you a general introduction to the analytical techniques needed for data science and is tailored for you to develop core maths knowledge that data science is built upon. It will enable you to assess your existing mathematical skills and sympathetically enable you to gain knowledge and skills and/or remedy any basic deficiencies. The learning topics mainly cover four areas: Basic functions, variables, equations and graphs; Linear algebra; Probabilities and Statistics. These topics will enable you to have a solid grasp over the most essential maths concepts as a data analysist and to explore applying the core mathematical models in a real-life work environment. The module is delivered by a mixture of classroom-based lectures and practical sessions. During and outside of scheduled class times you have remote access to a virtual learning environment where you will be able to access notes, participate in discussions, and experiment with some of the class material. Undertaking this module will enhance your employability skills towards various careers that make use of data science as they evolve and modernise, such as: human resources, marketing, psychology, finance, business administration.
View the full module definitionThis module introduces high-level computer programming using Python, one of the most powerful yet intuitive programming languages. It is designed to benefit students with no prior programming experience. This module enables you to understand the underlying concepts, principal components, design elements of computer programming and then it gradually leans towards technical aspects of programming by putting theoretical programming concepts into practice. This module employs real-world scenarios and case studies from various industrial domains and practices such as healthcare, marketing, finance, business, etc to induct programming concepts and skills. Best programming practice will be taught and used to ensure a maximum level of productivity and quality. In addition, methods and techniques will be introduced and applied to the validation and verification of software quality and standards.
View the full module definitionThis module presents an insight into the principles of data mining and machine learning. It equips you with essential skills in analysing data and drawing actionable insights for informed decision-making, leveraging mathematical and statistical models to tackle real-world problems. This module is designed to benefit students with no prior exposure to data mining and machine learning topics, by covering the fundamental principles and core concepts of data mining and the flow of the data in the machine learning pipeline. You will learn how to start looking at data from the perspective of the data scientist and apply machine learning techniques to various types of data with the goal of extracting valuable intelligence. You will explore and learn various data mining and machine learning tools and algorithms through real-world case studies from various industrial domains such as healthcare, finance, retail and IT. An in-depth understanding of various probabilistic and statistical models along with supervised and unsupervised machine learning techniques, including regression, ensemble learning, support vector machines, tree-based methods and the latest frontiers of machine learning, such as neural networks will be covered in this module.
View the full module definitionThis module provides an insight into the applications of data science. It is designed with no assumption of prior exposure to statistical data analysis and computer programming. It allows you to design and develop data-driven predictions models to extract meaningful information from seemingly unstructured and uncleaned data through probabilistic modelling and statistical inference. You will identify and deploy appropriate methodologies and modelling techniques in order to extract meaningful information for decision making and visualization. You will practice with various tools such as powerful Python libraries including NumPy, Pandas, Matplotlib, Scikit-learn and Seaborn to create end-to-end data analysis pipelines through real-world case studies from various industrial domains such as healthcare, finance, retail and IT. Moreover, you will acquire essential skills required for project management and planning, so that you can conduct scientific research. It will enable you to adopt appropriate methodologies and identify suitable technologies to address the research gaps, and thus formulate a sound project proposal.
View the full module definitionThis module consists of two parts. The first section of this module offers you an introduction to a real-world project brief that allows you to understand and appreciate the need for data science in professional and research environments. This will enhance your employability skills. During your project brief stage you'll familiarise yourself with real-world data science applications work with your supervisor as a team player, improve your key communication and personal skills, and appraise the relevance of your academic skills in real-life scenarios. You'll be expected to identify the topic of your major project/Masters dissertation, reflecting on how you can apply your knowledge of research methodology and project proposal preparation in defining your actual major project in a relevant applied data science area of your choice, relevant to your course. The second (main) section of this module supports you in the development, preparation and submission of a masters level project and dissertation. This represents a significant commitment and will be presented in a written document/report equivalent to a maximum of 10,000 words. The project topic will be assessed for suitability to ensure sufficient academic challenge and satisfactory supervision by an academic member of staff. The chosen topic will require you to identify/formulate problems and issues, conduct literature reviews, evaluate information, investigate and adopt suitable development methodologies, determine solutions, develop data science artefacts as appropriate, process data, critically appraise and present your findings. Regular meetings with the project supervisor will take place so that the project is closely monitored and steered in the right direction.
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