A data scientist must possess strong skills for learning the needs and preferences of users.
I was going to ask in the last section what qualifies as "quality data," but I think this is a good parameter to set. Considering both the needs of the user and the broader context of diversity together can ensure quality in a data set.
According to 2018 data from the US Bureau of Labor Statistics, released in 2018, only 26 percent of those in “computer and mathematical occupations” are women (US Bureau of Labor Statistics, 2019). Across all of those women, only 12 percent are Black or Latinx women, even though Black and Latinx women make up 22.5 percent of the US population. (Women of Color in Computing Collaborative, 2018)
This is another important distinction when considering how objective data science is.
First, I noted that ASCII can “encode all the letters of the (English) alphabet”; second, I mentioned that the “A” in ASCII stood for “American.” Early computer systems in the United States were built around American English assumptions for what counts as a letter. This makes sense… but it has had consequences!
This is something we talk a lot about in my Library Science classes. The perspectives we take for granted in our organization of data can cause a lot of unintended consequences on how it is retrieved, how people interpret it, and how biases can arise in every aspect of the organization of this information. However, I assumed that something as seemingly objective as data would be less prone to these issues, so it is good to have that assumption corrected.
I can apply that understanding—in conjunction with R programming—to completing practical projects.
I have never used any coding software or written code of any kind so I am excited to gain some knowledge in that area!