Open University

Pori

Data Mining for Business Analytics, Online teaching

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Intermediate studies

Faculty of Information Technology and Communication Sciences

Technology and Natural Sciences

<p>Apply for Open University studies by using the following links:</p><p><ul><li><a href="https://tau-avoin.ilmoittaudu.fi/lobby/?realization=J-otm-e70b8d02-b438-4820-8bfe-7213947436c2_2023-pori-DATAML370" target="_blank">DATA.ML.370 Data Mining for Business Analytics, online teaching, Pori</a></li></ul></p><p>You can find a link to the Open University Admissions service and application instructions on the <a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university/applying-open-university">Applying to Open University studies</a> page.</p><p>Some of the studies require prior studies. For example, studying intermediate studies may require some completed basic studies. These are described on the Information about studies tab of the study unit under prior requirements.</p><p>When you have been admitted to the studies, you must enrol separately via the Sisu information system for the implementation of the unit of study and, for example if set, for a practise group of your choice. You will receive instructions on how to do this by e-mail after you have confirmed your acceptance of the study place by paying the required fee.</p>

English

Online studies

<p>Look information at <a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university/applying-open-university">Applying to Open University studies</a>.</p>

<h5>Core content</h5><ul><li>The concept and terminology of data mining.</li><li>Understanding the special principles of processing large, non-structured datasets.<br></li><li>Basic principles of web search engines.</li></ul><h5>Complementary knowledge</h5><ul><li>Knowledge of the basic methods and algorithms.</li><li>Special challenges of processing large datasets: memory usage and data formats.<br></li></ul><h5>Specialist knowledge</h5><ul><li>Knowledge of the limitations of data mining.<br></li><li>Mapreduce algorithm.</li></ul>

The course gives an introduction to data mining and analysis of large datasets. For example, networks and databases involve massive amounts of data, and mining of useful information from data is an increasingly common challenge. By taking the course, the student learns the basic principles and terminology of data mining, knows the commonly used algorithms, and recognizes the typical challenges of processing large datasets. The course will introduce several application areas of data mining, including the principles of web search engines, recommendation systems, and web advertising.

<h5>Material</h5><ul><li>Type:&#160;Lecture slides</li><li>Author:&#160;P. Ruusuvuori</li><li>Exam material:&#160;Yes</li><li>Language:&#160;English</li></ul><h5>Material</h5><ul><li>Type:&#160;Book</li><li>Name:&#160;Mining of Massive Datasets</li><li>Author:&#160;A. Rajaraman, J. Leskovec, J.D. Ullman</li><li>Exam material:&#160;Yes</li><li>URL:&#160;http://infolab.stanford.edu/~ullman/mmds.html</li><li>Language:&#160;English</li></ul>

Data Mining for Business Analytics, Online teaching

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Data Mining for Business Analytics, 5 cr

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Data Mining for Business Analytics, Online teaching