16. Data Science & Presentation Skills - A Talk With Margot Gerritsen (Women in Data Science Worldwide & Stanford University)
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Listen and get insights into Data Science & Presentation Skills in this talk with Margot Gerritsen, (Founder & Former Executive Director Women in Data Science (WiDS) Worldwide, and Professor [Emerita] Stanford University).
We talk about: presentation framing and motivation; understanding your audience and the 2 - 3 messages you want them to remember; similarities between your presentation and writing a newspaper article; ending your presentation with a memorable takeaway; explaining where your data is coming from and how you look at the data; overcoming your fear of public speaking - you have nothing to lose, but you have something to gain; being honest about your data and the outcomes; practicing your presentation - the value of multiple dry runs; managing stage fright and anxiety; reducing presentation fears by inviting criticism early on - it will make you a better presenter.
Guest Info
- LinkedIn: Margot Gerritsen
- LinkedIn: Women in Data Science
Resources
- Book: Lies, Damn Lies, and Statistics (Michael Wheeler)
- Website: Women in Data Science
- YouTube: Women in Data Science
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Capítulos
1. Intro (00:00:00)
2. Presentation framing and motivation (00:01:40)
3. Understanding your audience and the 2 - 3 messages you want them to remember (00:08:50)
4. Similarities between your presentation and writing a newspaper article (00:09:38)
5. Ending your presentation with a memorable takeaway (00:11:47)
6. Explaining where your data is coming from and how you look at the data (00:12:59)
7. Overcoming your fear of public speaking: you have nothing to lose, but you have something to gain (00:14:59)
8. Being honest about your data and the outcomes (00:21:55)
9. Practicing your presentation: the value of multiple dry runs (00:25:27)
10. Managing stage fright and anxiety (00:26:15)
11. Reducing presentation fears by inviting criticism early on: it will make you a better presenter (00:28:33)
12. Outro (00:29:46)
17 episódios