Most Popular Slideshare Presentations on Data Mining
SlideShare data mining presentations cover many topics, offering a unique way of consuming data mining content and exploring a variety of slideshows, both narrow and broad in scope.
By Grant Marshall, Nov 2014
Slideshare is a platform for uploading, annotating, sharing, and commenting on slide-based presentations. The platform has been around for some time, and has accumulated a great wealth of presentations on technical topics like Data Mining.
Today, we will look at some of these top Data Mining presentations found on Slideshare. These presentations were retrieved by using a Python script and the Slideshare search_slideshow API, and then hand-curated to select the best, most relevant presentations. The slideshows and their associated metrics are shown below:
Here are some quick stats about the 24 slideshows in this table: there is an average of approximately 29,000 views, 621 downloads, 5 comments, and 38 favorites per slideshow. These aggregates can be deceptive, however.
With the comments, for example, a large number of these comments came from Machine Learning and Data Mining: 11 Decision Trees and TextMining with R. In the first case, most of these comments were requests for the slides (the author chose to disable downloads) and in the second case, most of the comments were requests for code that was excerpted in the presentation. Similarly, because some presentations had downloads disabled, the average download count is misleading. Adjusting for these comments and some downloads being disabled, the real averages are approximately 994 downloads and 3 comments per presentation.
Regardless, it appears that the social features are being put to use, and with some presentations, Analytics and Data Mining Industry Overview for example, the author can be seen responding to the comments. This shows how the format provides an interesting potential for people to interface with experts in data mining.
In this chart we see the diversity in the audiences that these different slideshows can draw. While there is a general upward trend in the number of favorites compared to the number of views, there are some exceptions. My hypothesis is that the more generally applicable lectures, like Data Mining: Concepts and Techniques, might draw a larger general viewership, the viewers will be less likely to favorite the slides. On the other hand, a more specific slideshow like Mining Social Data for Fun and Insight might not draw as large of a general crowd, but the viewers may be more likely to favorite it.
Related:
Slideshare is a platform for uploading, annotating, sharing, and commenting on slide-based presentations. The platform has been around for some time, and has accumulated a great wealth of presentations on technical topics like Data Mining.
Figure 1: Woordle of the tags associated with the presentations
Today, we will look at some of these top Data Mining presentations found on Slideshare. These presentations were retrieved by using a Python script and the Slideshare search_slideshow API, and then hand-curated to select the best, most relevant presentations. The slideshows and their associated metrics are shown below:
Here are some quick stats about the 24 slideshows in this table: there is an average of approximately 29,000 views, 621 downloads, 5 comments, and 38 favorites per slideshow. These aggregates can be deceptive, however.
With the comments, for example, a large number of these comments came from Machine Learning and Data Mining: 11 Decision Trees and TextMining with R. In the first case, most of these comments were requests for the slides (the author chose to disable downloads) and in the second case, most of the comments were requests for code that was excerpted in the presentation. Similarly, because some presentations had downloads disabled, the average download count is misleading. Adjusting for these comments and some downloads being disabled, the real averages are approximately 994 downloads and 3 comments per presentation.
Regardless, it appears that the social features are being put to use, and with some presentations, Analytics and Data Mining Industry Overview for example, the author can be seen responding to the comments. This shows how the format provides an interesting potential for people to interface with experts in data mining.
Figure 2: SlideShare Favorites vs. Counts
In this chart we see the diversity in the audiences that these different slideshows can draw. While there is a general upward trend in the number of favorites compared to the number of views, there are some exceptions. My hypothesis is that the more generally applicable lectures, like Data Mining: Concepts and Techniques, might draw a larger general viewership, the viewers will be less likely to favorite the slides. On the other hand, a more specific slideshow like Mining Social Data for Fun and Insight might not draw as large of a general crowd, but the viewers may be more likely to favorite it.
Related:
