As far as cleaning my data goes, I’m going to put the information into a slide presentation because it has loads of information, images for context, and links, which is perfect for google slides. On top of that, I’m going to mention which links are the source for multiple sections of the project, in case anyone is confused on where the source for each reference is. On top of that, I wrote all paragraphs, and I think I should shorten at least some of them to bullet points in order to make the project more presentable and more readable. What’s been enjoyable about the cleaning process was what was enjoyable about the start, which was writing about a topic that I am passionate about. What’s been difficult about it is figuring out which paragraphs I should keep, which paragraphs I should shift into bullet points, and which information from the paragraphs that I should take out. We all know that sometimes, less is more, however, it’s sometimes difficult to figure out what that “less” should be. It feels good to clean data because it lets you know that your previous work is good already, but there’s always room for improvement. Data cleaning allows your work to go from good to great, which is great for a learning experience. Some mistakes that I noticed from my data was that there was a thing or two that I didn’t label as well as that the paragraphs make the work seem unappealing. General trends that I’ve been noticing in data cleaning is that you always notice mistakes that you made that really baffle you. Mistakes that you can’t believe you made, like silly grammar errors or missing one of the small requirements. My data can be better visualized by, as I alluded to earlier, transitioning my paragraphs into bulleted lists in order to make my work cleaner and more presentable.
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