Applied Analytics: Big, Small, and Deep Data



The course gives methods for collection, analysis and presentation of big, small, and deep data.

• Introduction to the concepts of big, small, and deep data.
• Quantitative (big) data: Collection/generation (data mining), datafication (structuring and cleaning), and methods for analysis (e.g. boosting and random forest regression) using for example R.
• Qualitative (small and deep) data: Collection (e.g. non-participant observation, netnography, interviewing) and analysis (e.g. content analysis using for example nVivo, interpretism).
• Presentation and visualization of the results as a preparation for decision making.
ECTS-point10 ECTS