Introduction to data analytics.

There are two parts - lectures and practice sessions. Practice sessions require mandatory attendance. See the description in Moodle for details.607.916: Practice sessions 13:00 - 14:30607.926: Practice sessions 14:45 - 16:15We will use statistical software R (R Studio). It would be helpful to install it in advance: https://www.r-project.org/. (R Studio: https://posit.co/)

Motivation, Data Collection and PreparationData VisualizationAssociation Rule LearningCluster AnalysisRegression ModelsHeuristic MethodsArtificial Intelligence and Machine Learning

Sufficient prior knowledge in statistics as covered by Methodology II: Statistics.

A significant part of the lecture is covered by “Data Science for Business: What you need to know about data mining and data-analytic thinking” by F. Provost & T. Fawcett and by "Data mining: concepts and techniques" by  J. Han & M. Kamber.

Lectures: multiple choice online exam - 60 minutes time for 10 questions with 4 true/false statements each.Practice sessions: exercises; active participation in the discussion.Please note that both the successful completion of the practice sessions and the positive result from the multiple-choice exam related to the lectures are necessary in order to complete the Data Analytics VC successfully. If only one of the two parts is not completed successfully Data Analytics VC needs to be repeated altogether.See description in Moodle for details, especially regarding the practice sessions.

The exam is based on the actual contents discussed in the lecture.

Lectures: Of the possible maximal 40 points you must reach at least 28 points to get a positive grade.Practice sessions: Based on the checklists and presentations of exercises you must reach at least 60% = 15 points of the possible maximal 25 points to get a positive grade.Lectures contribute 2/3 and practice sessions 1/3 to the total grade.Please note that both the successful completion of the practice sessions and the positive result from the multiple-choice exam related to the lectures are necessary in order to complete the Data Analytics VC successfully.