In most data science-related problems, datasets consist of multiple variables, in which independent variables might depend on other independent variables. When the variables in datasets represent observations at different times, we call this dataset a time series set. The time interval in these data sets may be hourly, daily, weekly, monthly, quarterly, annually, etc. One…… Continue reading When mere correlations are not enough: The Granger Causality test
Month: September 2022
SIG-KM International Research Symposium 2022 Keynote
Super time at the SIG-KM International Research Symposium 2022, hosted online by the University of North Texas. Thanks to Jeff Allen and to Lu An for the invitation! Thanks also to the Center for Studies of Information Resources (CSIR) of Wuhan University (WHU) and other academic sponsors for their support of the symposium!
Comparison of Google Analytics and SimilarWeb for Web Analytics
Approaches to collecting website analytics data can be grouped by the focus of data collection efforts, resulting in the emergence of three general methodologies, namely: user-centric, site-centric, and network-centric. Two industry standard and popular web analytics platforms are Google Analytics and SimilarWeb. Google Analytics is a site-centric service, and SimilarWeb is a user-centric service that…… Continue reading Comparison of Google Analytics and SimilarWeb for Web Analytics
User-centric, site-centric, and network-centric: Approaches to collecting website analytics data
Approaches to collecting website analytics data can be grouped by the focus of data collection efforts, resulting in the emergence of three general methodologies, namely: (a) user-centric, (b) site-centric, and (c) network-centric. The central traits of each are as follows. User-centric: Web analytics data is gathered via a panel of users, which is tracked by software installed on…… Continue reading User-centric, site-centric, and network-centric: Approaches to collecting website analytics data