Data Acquisition and Cleaning:
I extracted channel data, including video information, likes, comments, and views.
Data inspection revealed inconsistencies: duplicate entries, irrelevant data fields, and date/number formatting errors.
To ensure data quality, I implemented data cleaning techniques to:
1. Remove duplicates.
2. Eliminate irrelevant data fields.
3.Standardize date and number formats.
Data Transformation and Analysis:
To assess audience engagement for each video, I transformed the data by calculating an engagement rate. This metric was derived by summing the number of likes and comments, then dividing the total by the number of views.
Microsoft Excel's descriptive statistics functions were employed to analyze the cleaned data, providing insights into key metrics like average views, likes, and comments per video.
Data Visualization:
To further enhance understanding of the channel's performance, I created data visualizations using slicers and graphs within Excel. These visualizations allowed for interactive exploration of the data, revealing trends and patterns in engagement metrics.
This project demonstrates my ability to:
Extract and clean data from online sources.
Transform data to create meaningful metrics.
Analyze data using statistical methods.
Communicate insights through data visualization.
By following these data analysis best practices, I was able to gain valuable insights into the engagement of the "Online Math Tv" YouTube channel.
