Merav Yuravlivker | Gettting People Invested in Data Science
28 October 2021, 05 pm

Merav Yuravlivker | Gettting People Invested in Data Science

CEO of Data Society

Episode notes

Merav Yuravlivker, CEO of Data Society, describes how to get people excited about data science internally. Also, how data science teams should think about defining and measuring success.

Data Society is a leading provider of customized data science training programs and AI/ML solutions for enterprise and government agencies (datasociety.com)

A few key snippets from our conversation:

01:13 - Start of session
7:33 - Ways to get people invested in data science internally
13:00 - Measuring and defining success with data science projects
15:50 - Growing engagement at internal community events / lunch & learns
32:38 - Building to serving predictive models
47:47 - Calming fears around the company "being behind" in using data
58:50 - Doing both the A & B of A/B Testing

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Featured in this episode
Rachael Dempsey
Rachael DempseyHost
Community Manager at RStudio
I love connecting people across the data science community to share what they're accomplishing with data and help others do the same through community discussions, industry meetups, and more.
Merav Yuravlivker
Merav YuravlivkerGuest
Co-Founder & CEO at Data Society
Co-Founder & CEO of Data Society. A former teacher, Merav is passionate about empowering professionals with the data skills they need to transform their industries, solve tough challenges, and build innovative communities.

Frequently asked questions

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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?

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