Why regression still matters | Keith McNulty | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Keith McNulty, Analytics Leader at McKinsey & Company, to chat about his unique career path into data science, the importance of regression and network analysis, strategies for self-learning and balancing work, and navigating the evolving landscape of AI. In this Hangout, Keith talks about navigating the evolving landscape of AI, including trustworthiness, risks, and its impact on the data science profession. Keith highlights that AI is rapidly changing and discusses the balance between acquiring technical AI development skills and understanding the trustworthiness and inherent risks of large language models. He suggests prioritizing knowledge of AI's reliability, as these challenges are likely to endure, and recommends identifying trusted sources to sift through the hype. The discussion also covers AI's capacity to identify complex patterns in large datasets, which can be beyond human capabilities. However, it also acknowledges AI's tendency to be convincingly incorrect. A significant point raised by Keith is his concern that data scientist could be pushed more towards software engineering, and he asked practitioners to consider if they are still engaging in the "science" aspect of data science. Resources mentioned in the video and zoom chat: 🔗 Keith McNulty on LinkedIn → https://www.linkedin.com/in/keith-mcnulty/ 🔗 Keith's Regression Book (Handbook of Regression and People Analytics) → https://peopleanalytics-regression-book.org/ 🔗 Keith's ONA/Graph Book (Handbook of Graphs and Networks in People Analytics) → https://ona-book.org/ 🔗 PositConf Registration (including virtual option) → https://posit.co/conference/ 🔗 "Storytelling with Data" book → https://www.storytellingwithdata.com/ If you didn’t join live, one great discussion you missed from the zoom chat was about the evolving role of data scientists and the importance of "putting the science back in data science". Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! 💛 00:00 Introduction 00:46 "Will you let us tell us a little bit about yourself, what you do, what you like to do for fun?" 03:10 "Were you that active (on social media) before you were in data science?" 05:33 "I was just wondering if you could maybe talk a little bit about your most favorite resources that you used while retraining yourself as a data scientist?" 10:12 "You wanna give a quick pitch for both of (your books) so we have some context?" 14:52 "What do you think about the possibility of extracting latent character traits about people from text?" 19:51 "How do you balance self learning with working time?" 25:16 "What kind of questions are you using networks to ask in the people analytics space?" 30:09 "What would you say is the most important AI related skill to learn for the future and to stay ahead in the workplace?" 35:59 "I'm interested in how talent is measured among white collar workers. Could you share some of the metrics you use and how you address the limitations of more straightforward measures like time in the office?" 40:26 "So I was wondering if that (early) role, that engagement role, helped you bridge this switch from academia to industry, or, how did that role help you choose the skill you wanted to build." 43:54 "I just wanted to get your perspective on imposter syndrome, especially for people who are making some sort of transition in their career into data science." 47:00 "Where do you source your daily math problems?" 48:19 "Do you have any thoughts on policy for AI in the current setting and and also, specifically thinking about there's a lot of people in my company that are hesitant because of the environmental impact of AI." 51:32 "What did we not talk about that you wish we had talked about?"