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S2E26: Interview with Gábor Békés, International Economist and Author at CEU
Manage episode 373611482 series 3343922
This week of the Mixtape with Scott, I have the pleasure of introducing you to Gábor Békés, an associate professor at Central European University in Austria and author of an exciting new textbook in data science and causal inference entitled Data Analysis for Business, Economics and Policy (Cambridge Press 2021).
I wanted to talk to Gábor for many reasons — one because I am interested in talking with people whose roles in the scientific production function is to create platforms of knowledge sharing. These include editors of journals, department chairs, organizers of conferences, and authors of textbooks. I interviewed Jeff Wooldridge at the start of this year, I’m interviewing Bill Greene later this year, and I’m interviewing Gábor today.
And on that point, I also wanted to talk to him about how his book, written with the late Gábor Kézdi who passed away around the time of the book’s publication, came about, what it was about, and who he sees his ideal audience to be. The book is a nearly perfect, flawless piece of writing. Not just in its pedagogy and what feels like an effortless precision, but also in what they cover and how they cover it. Someone who has never really worked with data before could take this book and move from the most fundamental issues around exploring data, from data collection and ensuring data quality, to data visualization, to learning canonical regression models, then moving into more advanced and contemporary areas like machine learning based predictive analytics and causal inference.
The care and precision of the book is reflected in its aesthetic too. It’s simply one of the most beautiful books to the touch I’ve seen — the purple and green colors, its width, the glossiness of the pages, and the rich opportunities to learn R coding — are just a wonderful delight. I highly recommend everyone own a copy, and consider assigning it this fall for your statistics courses. It’s the perfect companion, if not the actual textbook.
But I also just wanted to learn Gábor’s story, and I got to learn at least some of it. I learned about him growing up in Hungary during the throes communism in his formative years and how witnessing first hand a regime transition shaped his desire to learn economics which ultimately led him into study economic geography and international economics.
Thank you again for tuning in. I hope you enjoy this week’s interview as much as I did!
Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Get full access to Scott's Mixtape Substack at causalinf.substack.com/subscribe
114 episodes
Manage episode 373611482 series 3343922
This week of the Mixtape with Scott, I have the pleasure of introducing you to Gábor Békés, an associate professor at Central European University in Austria and author of an exciting new textbook in data science and causal inference entitled Data Analysis for Business, Economics and Policy (Cambridge Press 2021).
I wanted to talk to Gábor for many reasons — one because I am interested in talking with people whose roles in the scientific production function is to create platforms of knowledge sharing. These include editors of journals, department chairs, organizers of conferences, and authors of textbooks. I interviewed Jeff Wooldridge at the start of this year, I’m interviewing Bill Greene later this year, and I’m interviewing Gábor today.
And on that point, I also wanted to talk to him about how his book, written with the late Gábor Kézdi who passed away around the time of the book’s publication, came about, what it was about, and who he sees his ideal audience to be. The book is a nearly perfect, flawless piece of writing. Not just in its pedagogy and what feels like an effortless precision, but also in what they cover and how they cover it. Someone who has never really worked with data before could take this book and move from the most fundamental issues around exploring data, from data collection and ensuring data quality, to data visualization, to learning canonical regression models, then moving into more advanced and contemporary areas like machine learning based predictive analytics and causal inference.
The care and precision of the book is reflected in its aesthetic too. It’s simply one of the most beautiful books to the touch I’ve seen — the purple and green colors, its width, the glossiness of the pages, and the rich opportunities to learn R coding — are just a wonderful delight. I highly recommend everyone own a copy, and consider assigning it this fall for your statistics courses. It’s the perfect companion, if not the actual textbook.
But I also just wanted to learn Gábor’s story, and I got to learn at least some of it. I learned about him growing up in Hungary during the throes communism in his formative years and how witnessing first hand a regime transition shaped his desire to learn economics which ultimately led him into study economic geography and international economics.
Thank you again for tuning in. I hope you enjoy this week’s interview as much as I did!
Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Get full access to Scott's Mixtape Substack at causalinf.substack.com/subscribe
114 episodes
All episodes
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