We were recently joined by Dr. Kobi Abayomi, Sr VP of Data Science & Analytics at Warner Music Group.
Here are a few snippets from our conversation:
1:19 - Start of session
3:42 - Example of using data science in the music industry
6:32 - Data science compared to data analysis: going from analysis to research
9:35 - Data science and data engineering, what comes first?
11:42 - How did you become a Bayesian shop?
17:05 - Productionalized visualizations being outside of data science
18:45 - How do you explain quantitative concepts to others across the business
26:12 - When the metrics used by the business aren't what they should be
30:39 - How algorithms will (or won't) impact music
39:59 - Gathering data from the business
41:33 - Becoming a leader, your team is a gift
A few resources mentioned by Kobi:
Illustration of every genre of music that came up: everynoise.com
I like Qtip’s radio station on AppleMusic
I try to listen to every interview every of Donald Knuth
An audience-led conversation held on Zoom focused on data science leadership that is open to all every Thursday at 12 ET. You can check out the recordings on this site as well! Learn more on the About DSH page here:https://www.rstudio.com/data-science-hangout/about
You don't have to register to join. You can add the event to your own calendar here:https://www.addevent.com/event/Qv9211919
There are three ways to ask questions during a live session:
All of the recordings from past sessions are shared here:https://www.rstudio.com/data-science-hangout/live-calls
That's up to you. The host, Rachael usually opens up the discussion by asking the featured leader what they are most excited about in data science currently, and then we turn the questions over to the audience. Here are a few examples of questions that have come up before:
What is the divide between a business analyst and a data scientist?
What is the most unique problem you've had to solve in your industry?
My leaders refer to our BI tools when asked about data science, do you consider BI data science? Why/why not?
Did you always have managing skills/people skills, or did you have to learn them?
Could you define levels of data science maturity for companies?