3 Takeaways from #DataFemme Season One
So as some of you know, I wrapped up recording for #DataFemme Season One today. I can’t wait to release the Season Finale of #DataFemme featuring Senior Developer at Small Planet, Quinn McHenry, on Friday. It will be followed by a special bonus episode as you eagerly await Season Two.
So as you inevitably give into your curiosity and listen to all the Season One episodes you missed, here’s three main things that I’d want you to take away from #DataFemme’s first season.
Perfect is Great, But Meaningful is Better
No one who saw the state of my room would ever say I’m a perfectionist, but they’d be wrong. I can be extremely perfectionist when I’m dealing with something I care about. Unfortunately, that hyper focus can be as detrimental to my achievement as a lack of focus would be. Throughout this season, I’ve cringed a little bit at every hint of background noise or uneven transition left in the final product. And often times, that made me less able to market these episodes as much as they deserved. When my dad told me that the background noise in one of the episodes doesn’t take away from the content of the episode, I took a step back enough to realize that the content really is what’s key.
We shoot for perfection while creating, but when the product is finished, nitpicking takes away the best part of the experience. Others will enjoy your content regardless because of the work you put in. But the person who should be most proud is you.
Some Diversity is Unseen
Early into recording #DataFemme, I realized that I’d been sleeping on some very important aspects of diversity. I naturally am drawn towards promoting the work and stories of women, non-gender binaries and people of color - something I will continue to do for the majority of my episodes. But I found that in opening up my coverage to include those who may not fit into any of those categories, I discovered so many other types of diversity that affect our lives as data scientists.
For example, there are several people for whom English is a second language. This might not be clear to their peers at university, but it poses a challenge when attempting to learn to code with materials that only exist in English. By translating materials like my friend Gustavo Alvarenga is doing with Hadley Wickham’s “Advanced R”, we promote inclusion in our space. It takes time and it takes effort. And all of it is beyond worth it to make sure that our field of AI is as representative of our global community as it can be.
Data Science Always Existed
I have really enjoyed interviewing people at the beginning, middle and prime of their data science careers. There are insights to be heard from every level. What I’ve realized, from some of my guests, is that data science has been in existence for a long time, even if we didn’t have a formal name for it. There are people who have been working with data within their respective fields for years, hoping that their peers recognize, as Layla Bouzoubaa said, that “Data is Liquid Gold.”
Now that the value of data and data science is celebrated in society, where do we go from here?
What were your takeaways from Season One of #DataFemme?
I want to know. :) So please share with me in the comments here, by email to dikayo@dikayodata.com or on Twitter at @DikayoData. I’d love to hear what you have to say and what you want to see more of in Season Two.