Data Mining - CMI7507

Assessment 1: Other

During the module there shall be a series of checkpoints where learners can seek guidance and feedback from tutors. These checkpoints shall include opportunities for feedback when choosing an approach and selecting appropriate literature for review as part of the assessment tasks.

Other

Assessment 2: Portfolio

Individually produce a portfolio that consists of a literature review and an evaluative report documenting a solution to a practical data mining problem.

Portfolio

Assessment 1: Portfolio

On-line discussion forum - this will be a series of short contributions relating to key concepts, with a view to encouraging engagement with the content. Assessment will be based on engagement rather than the quality of the response.

<p>Data mining is a collection of tools, methods and statistical techniques for exploring and extracting meaningful information from large data sets. It is a rapidly growing field due to the increasing quantity of data gathered by organisations. There is a potential high value in discovering the patterns contained within such data collections. In this module you will look at different data mining techniques and use appropriate data-mining tools in order to evaluate the quality of the discovered knowledge. You will study approaches to preparing data for exploration, supervised and un-supervised approaches to data mining, exploring unstructured data and social impact of data mining. You will be expected to develop their knowledge such that they are able to contribute to discussions around current application areas and research topics and to increase your background knowledge and understanding of issues and developments associated with data mining.</p>

School of Computing and Engineering

<p>https://library.hud.ac.uk/readinglists/CMI7507</p>

Computing and Information Systems

CMI7507 - Data Mining

Data Mining

Justify and critically discuss the key concepts of data mining (including legal implications such as GDPR) and the breadth of areas of application.

Interpret and evaluate the results of the analysis to draw conclusions and make informed decisions.

Select appropriate data mining techniques in order to enable exploration of large datasets.

Make appropriate modifications to large datasets to prepare the data for analysis and exploration.