We were recently joined by Frank Corrigan, Director of Decision Intelligence at Target.
6:38 - Problem formation - trying to find the unknown unknowns and bringing them to the business
8:20 - Integrating your data science team into your company's business objectives (ex. newsletter)
10:50 - What is the divide between a business analyst and a data scientist, in your eyes? How business analysts and data scientists differ
15:32 - What is the biggest mistake you’ve made in your role and what did you learn from this mistake?
19:15 - Onboarding new team members effectively
25:00 - The importance of motivating non-data scientists
26:42 - Resources for data scientists
32:30 - Challenges when using different tools across a data science team
49:28 - Analytical thinking vs critical thinking skills
57:30 - Embracing the 80/20 Rule & the importance of Focus Time
1:02:48 - Two frameworks to be more effective with stakeholders
1:06:47 - Rebranding to "decision intelligence"
1:08:35 - Quantitatively measuring impact from data science insights
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?