The course is taught concurrently with the 'Social Web: (Big) Data Mining' course for undergraduate students (JSB454): bit.ly/socialwebcourse or bit.ly/socialwebdatamining. Besides application of advanced analytical procedures, graduate students should prove themselves more autonomous and flexible, being exposed to and being able to obtain variety of data, process them, quickly find their feet, and perform exploratory analysis as a basis for drawing conclusions for decision-making and/or subsequent automation and prediction employing machine learning models - in accordance with the first part of the planned broader and interdisciplinary two-semester course 'Data Mining and Machine Learning': bit.ly/datamachine.
In the weeks where face to face sessions do not take place, the students can utilize open educational resources suggested for each session and attend a webinar to reinforce their skills. Both master and bachelor students can work on their research projects (final examination) in cooperation with the partner of this course, KPMG Czech Republic (home.kpmg.com/cz/en/home.html).
NOTE: Based on the number of enrolled students and the classroom occupancy limitations, please let me know that you are on the waiting list at jameslittlerose@gmail.com.
