Data Mining

The data mining process, based on the widely adopted CRISP-DM approach, consists of six phases. It starts with business understanding, where a business problem or opportunity is translated into a data mining project. The subsequent phases involve data understanding, data preparation, modeling, evaluation, and deployment. Iteration is highlighted as a common practice throughout the process. Each phase requires specific skills, including a deep understanding of the business, knowledge of databases, and proficiency in data mining methods. The module also categorizes data mining methods into supervised (classification, regression, time series analysis) and unsupervised (clustering, association analysis) types. Various techniques and algorithms are mentioned, with a focus on the goals each method aims to achieve.

Finally, the module emphasizes the importance of choosing the right data mining tools, such as commercial options like IBM SPSS Modeler and SAS Enterprise Miner, as well as free/open-source tools like RapidMiner, KNIME, WEKA, and Orange. Programming languages like Python and R are also noted for data analysis and modeling, with KNIME and R specified for use in the course. The overall goal of this module is to provide students with the skills needed for successful data mining projects, aligning with business objectives, and leveraging effective tools and methodologies.