We were joined by Elaine McVey, VP of Data Science at the Looma Project.
Communication was a focus in our conversation with Elaine as we discussed improving communication skills as a data scientist, packaging insights to executives, and what people often get wrong when sharing insights with the business.
What are your stakeholders doing today that they should change as a result of your analysis? What will the business impact be of doing so?
How can you deliver more value as a data scientist? Focus on communicating the results of your analysis, not the technical details.
Here a few snippets from the conversation:
21:30 - How to approach experimentation and running tests
43:30 - How do you communicate the value of data science to executives
52:40 - Ways to improve your communication skills as a data scientist
57:15 - How to package insights to executives
1:01:03 - What data scientists get wrong when communicating 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?