Computing students are among those greatly affected by the mode of learning transition due to the Covid-19 pandemic. To this writing, there are no formal studies conducted yet to describe and visualize the effect of this transition on the computing students’ academic performance in particular. In light of this reason, this study was initiated to effectively use data analytics to describe and visualize the effect of the mode of learning transition on the computing students’ academic performance. Anchored to the objectives of the study, a mixedmethod approach where quantitative data, 2,997 final grades of BSIT students of Camarines Sur Polytechnic Colleges were collected and a qualitative method was applied to describe the perceived factors affecting academic performance, retention, and dropout rate. RStudio was used to analyze and visualize the computing students’ academic performance within three (3) semesters both before and amidst the Covid-19 pandemic. Data analytics revealed that there is no significant difference in the academic performance of the students before and amidst the pandemic (p-value= 0.5798). However, in terms of retention and dropout rate, it was revealed that there is a significant difference before and amidst the pandemic (p-value=1.553e-0). Implementation of thematic analysis further revealed the perceived factors that affect the academic performance, retention, and dropout rate. Identified themes revealed that resources, self-factor, learning environment, and financial factors are the top factors that affect computing students’ academic performance, retention, and dropout rate. Therefore, an appropriate and prompt intervention in consideration of these revealed factors is vital.