In the realm of digital media, the influence of YouTube as a content platform cannot be underestimated. In the pursuit of generating data-driven insights and putting my learnings into a project, I embarked on a data analysis of three distinct YouTube channels. These channels, namely Ken Jee, Tina Huang, and Alex the Analyst, have garnered substantial attention within the data community and I will be using their data for this exploratory analysis project.
Data Collection : The data for this analysis has been obtained using the YouTube Data API.
Data Analysis & Visualization :
- Channel Metrics and Significance - A foundational understanding of each channel’s scale - their audience reach and content library size within the YT ecosystem.
- Metrics Interplay and Correlations - Exploring the correlations and relationships among engagement metrics - views, likes and comments.
- Content Consistency & Frequency - Investigating the monthly average uploads for each channel to reveal the pattern.
- Video Publishing Trends - Revealing the strategic decisions behind the channels' preferred days and times for content distribution. This analysis highlights the synchronization between publishing schedules and audience behavior.
- Video Upload Trends - Identifying a panoramic view of the evolution of the video uploads and a visual narrative of content consistency and strategic timing on a macroscopic scale.
- Top Performing Videos Across Channels - Identifying and ranking the top-performing videos from each channel based on a combined evaluation of views, likes, and comments.
- Audience Engagement Spectrum - A comparative analysis, employing box plots to characterize the spectrum of audience engagement.
- Video Length Impact on Viewer Engagement - Exploring the relationship between video duration and engagement metrics to evaluate the implication of content duration and its effects on viewer likes and comments.
- Descriptive Statistics for the engagement metrics - Calculating descriptive statistics (mean, median, standard deviation, etc.) for key engagement metrics such as likes, comments to draw a summary of the central tendencies and variability of these metrics, and to understand the typical performance of each channel.
Have a look at the report in this repository to find the result of this analysis.