Way back in August, I was lucky enough to be invited to be a guest on Kirill Eremenko’s SuperDataScience podcast. Since then, my interview has been downloaded over 8000 times (I’m famous!) Kirill was in London the week my episode was out, so I finally met him in person at a dinner meetup with his students.
I was blown away by the response to the interview. I have to admit, I was skeptical when he asked me to come on the show within months of landing my first role in data science. I wondered what my experiences could possibly contribute to a podcast that had interviewed data science trailblazers and leaders. (I decided my strategy to bring value to the podcast would be to share lots of tips about getting started with Tableau.)
Kirill thought that my story could inspire his listeners who were looking to break into the field. I can see the podcast slowly shifting in that direction now, and his conference in San Diego last week shows me that that his target audience is more aspiring data scientists, which is an obvious choice from the courses he offers, which help his audience upskill in industry-relevant areas.
Nevertheless, I still approached the podcast with much trepidation. I am a self-conscious, nervous speaker, and I need time to formulate answers. I dreaded hearing my own voice. I was relieved, upon review, to find that my voice was not as nasal as I imagined it to be, my accent not as grating. I would love to learn to speak more succinctly and directly. I know that is something I need to work on.
The interview was a great opportunity for me to reflect on what I had learnt in my time at the Data School. The gulf is immense. I feel like my brain has been restructured!
Kirill asked what I learned about Tableau in my 4 months at Data School beyond his courses that I took (to help me learn enough to apply to Data School) (and I answered the question by talking about Alteryx….! Doh!) I really wanted to say that in terms of Tableau knowledge, I learned mostly about best practices. Part of this was exposure – using Tableau every day helped me become very familiar with where to find things, as did learning tricks and hacks.
Something a bit more intangible that I learned at Data School was probably more around data viz best practice – what makes a viz more user friendly, accessibility issues, different things to think about in terms of what things you want to draw the user’s attention to and how to do it, how to encourage a user to interact with a viz, where things might be best placed on a dashboard, and thinking about different ways of expressing an idea (and therefore being able to choose the best way to do so). Again, this comes to some extent with exposure – learning to critique vizes, and looking at lots and lots of them to understand what works and what doesn’t, and why.
We learned about ways to be more creative (sketching). We also did a lot of presentations. We gained experience in presenting to different audiences. I did mention this (in response to another question) as well as discussing the consultancy skills that we gained.
Next up: Apologies to Chloe Tseng for getting the #vizforsocialgood hashtag wrong and not knowing enough about it! I have since refreshed my knowledge about this very good cause! Social projects apply through the website for their data project to be featured and this goes out to data enthusiasts around the world who then publish their work on Twitter for all to see. The organization then picks one or more of the visualizations for use on its communication channel.
Since the interview went live, I have been pleasantly surprised to have had many people reach out to me to tell me how much they enjoyed the interview, how inspired they were (Kirill was right!), as well as tell me they intended to apply for the Data School, and it was exactly what they were looking for!
Vizterious Girl 