AI Product Development Framework
Data Leaders
Ivy Nguyen shares insights on:
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Early Stage AI Startups and Product Market Fit
Data Leaders
In this episode, I invite Swaroop "Kittu" Kolluri to share his thoughts on:
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Building a Data Startup
Data Leaders
In this episode, Tom shares his advice on:
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Grow Like Dropbox: Data Inspired Viral Marketing
Data Leaders
In this episode, I chat with Savvas Zortikis about an Analytical approach to Viral Marketing.
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Crafting Engaging Content: A Data-Inspired Approach
Data Leaders
In this episode, I sit down with Aaron to learn about how he and his agency creates engaging content. Takeaways: Focus on copywriting basics. Make every word valuable. This means not using words like “just” or “really.” People love a good narrative. Give them a gift of emotional engagement. Pull the strings of your heart without holding back. Find personal stories from your daily work and translate that to something of value to people. This means approaching your day like a book, with each meeting as a chapter of a story.
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Content Creation & Analytics: Writing Process for Top Writers
Data Leaders
I've always wondered if there were any patterns among the top writers when it comes to their writing process.
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Data, Intuition, and Product Development
Data Leaders
Olof Mathe, the founder of Mixmax, one of the most well-renowned email communication products, shares his thoughts on:
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Attributes of High-Quality Content
Data Leaders
In this podcast series, I sat down with the top writers from SF, LA, and NY to learn how they create great content.
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Data Inspired Product/Market Fit
Data Leaders
I met up with Sean Sheppard to learn about his growth based product/market fit approach.
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Content Creation & Analytics - Part I
Data Leaders
In this podcast series, I sat down with the top writers from SF, LA, and NY to learn how they create great content.
info_outlineFor the past 2 years, Zach has been working as a data scientist at an industry leading data consulting firm. He works in fraud analytics space where he and his team has saved hundreds of millions of dollars of federal dollars using sophisticated data science techniques. He is also a recent graduate of data science program at UC Berkeley.
When I met him, I was really impressed with your ability to speak “real world” data science and later I found out that he has a professional background in teaching complex topics like physics and calculus, which is what makes you such a good communicator in this field.
I sat down with him on a sunny Saturday afternoon to discuss one of the most exciting projects he has worked on in his data science career.
Here's a quick recap of what we discussed:
- Can we predict what pitch is going to be thrown next in major league baseball? Implications for Hitters (batters) equipped with this data is $10M to $15M per season.
- A wave of In-game analytics about to hit the sports industry. This in-game analytics may eat ‘Moneyball’ style static analytics for breakfast
- Are better pitchers tough to predict? Or are they just as easy to predict as others?
- What’s the correlation between a pitcher’s ERA and his predictability? ERA is a baseball metric - earned runs average - its used to gauge how well a pitcher is doing in a season.
- Is it better to be 90% accurate 30% of the times or 30% accurate 90% of the times?
- What has Ashton Kutcher to do with Data Science and Social Good?
- How to cultivate the presence of mind when communicating about data?