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Investigate state of the art industry and research trends in business intelligence. Conducting level 9 research and how to communicate results. Evaluate the role and benefits of effective business intelligence in the organisation. Demonstrate awareness and critical understanding of developments in data warehouse design and implementation.
Demonstrate awareness and critical understanding of developments in business intelligence front end tools and techniques. Independently research current trends and developments in business intelligence related technologies.
Apply research methods to their work and differentiate between exploratory, constructive and empirical research. Evaluate and critique current legislation on data privacy and relevant ethical issues.
Data Mining Algorithms Module aims: To study advanced concepts relating to data science. Using both lectures and independent research, the module will address a number of issues relating to understanding and optimising the performance of data mining algorithms.
Discuss in depth a variety of data mining techniques, and their applicability to various problem domains. Evaluate a business objective and related dataset to assess the appropriateness of a number data mining algorithms in achieving that objective.
Work through the mining and evaluation stages of a data mining methodology, selecting the most appropriate mining technique, and optimising algorithm parameters to maximise performance Independently research current trends and developments in knowledge discovery related technologies.
Critically analyse relevant publications to assess the relative merits of methodologies used and conclusions made. Investigate state of the art and research trends in text mining and web content mining.
Critique and evaluate the performance of algorithms for both text mining and web content mining.
Demonstrate an awareness and critical understanding of ways to extract key concepts and relationships from semi-structured and unstructured text, and structure them for data mining.
Discuss current research activities relating to text mining and web content mining.
Understand limitations of current information extraction techniques and the vision for the future. Extract key concepts and relationships from semi-structured and unstructured data.
Apply prediction and clustering techniques to the prepared data, and critically evaluate the results. Independently research current trends and developments relating to the processing of semi-structured unstructured data.
Data Science Applications Module aims: Apply state of the art business intelligence, data preparation and data mining techniques to a specific case study and dataset. Starting with a business objective and data, work through all stages of an appropriate methodology to extract knowledge from the data in accordance with the business objectives, and present the results to stakeholders in the appropriate language, highlighting how the knowledge learned can be used to add value to the business.
Research appropriate business intelligence or data mining techniques for a specific problem domain. Select from, and apply, a range of advanced, state of the art, data analysis, data visualisation and data mining techniques to a practical case study.
Understand and interpret a business objective, and translate the business objective to business intelligence and data mining objectives. Identify possible risks and limitations of a data set in achieving business objectives.A doctorate (from Latin docere, "to teach") or doctor's degree (from Latin doctor, "teacher") or doctoral degree (from the ancient formalism licentia docendi) is an academic degree awarded by universities that is, in most countries, a research degree that qualifies the holder to teach at the university level in the degree's field, or to work in a specific profession.
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