Marck Vaisman speaks to Jon Krohn about his paradigm for understanding core data practitioner types. Hear Marck detail the four data practitioner personas that he has identified in his research, why he believes the roadmaps that influencers like to promote as surefire ways to a data science career don’t work in practice, and why the term “data scientist” is still so elusive and hard to recruit for.This episode is brought to you by Gurobi, the Decision Intelligence Leader. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.In this episode you will learn:• How Marck started his work in defining data science roles [08:06]• The relationship between the four data practitioner personas [15:26]• About Marck’s “menu” for effective data science [40:43]• How recruiters can hire the best data scientist for the job [59:31]Additional materials: www.superdatascience.com/821

Jon Krohn takes OpenAI’s new models (o1-preview and o1-mini) for a spin in this Five-Minute Friday, learning their key strengths and limitations, and how the o1 series may represent yet another landmark for generative AI.Additional materials: www.superdatascience.com/820Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

SuperDataScience veteran and Udemy teacher Luka Anicin is on the podcast to talk about his brand-new course, “PyTorch: From Zero to Hero”, available exclusively on superdatascience.com. Host Jon Krohn asks Luka why he feels that every data scientist should consider PyTorch as their default Python library, and why “keeping it simple” can secure the success of a machine learning project.This episode is brought to you by AWS Inferentia and AWS Trainium, and by Gurobi, the Decision Intelligence Leader. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.In this episode you will learn:• About the PyTorch library [03:29]• Why PyTorch became so popular [25:24]• How to increase accuracy and efficiency in PyTorch [31:49]• How to utilize transfer learning [35:44]• Why real-world projects are essential to data scientists [41:10]• About Datablooz [46:49]Additional materials: www.superdatascience.com/819

Experts from AI and data science discuss the impact and benefits of decentralization, the importance of structuring AI systems in business, and why knowing the basics will always matter for data engineers. Listen to Shingai Manjengwa (episode 809), Daniel Hulme (episode 807), Jerry Yurchisin (episode 813) and Nick Elprin (episode 811) explore a future world of work that rewards continuing learners, sets tasks for the people best suited to complete them rather than those whose job titles reflect the spec, and applies a fleet of ‘AI agents’ to solve complex business tasks.Additional materials: www.superdatascience.com/818Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

Dr. Julia Silge, Engineering Manager at Posit, introduces the brand-new Positron IDE, perfect for exploratory data analysis and visualization. She also lays out her top picks for LLMs that boost coding efficiency and discusses when traditional NLP methods might be the smarter choice over LLMs. Plus, Julia highlights some must-know open-source libraries that make managing MLOps easier than ever. Tune in for insights that every data scientist, ML engineer, and developer will find useful.This episode is brought to you by Gurobi, the Decision Intelligence Leader, and by ODSC, the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.In this episode you will learn:• Overview of Posit and Positron IDE [05:20]• How the needs of a data scientist differ from those of a software developer [10:54]• How to contribute to the open-source Positron [19:50]• MLOps and Vetiver: Tools for deploying and maintaining ML models [37:01]• Natural Language Processing (NLP) and the Tidyverse approach [50:34]• The role of AI and LLMs in data science education [1:24:18]Additional materials: www.superdatascience.com/817

Jon Krohn takes on a listener's challenge to explain his work in data science to his 94-year-old grandmother, Annie. This heartwarming conversation covers what data is, the role of a data scientist, and breaks down artificial intelligence (AI) and artificial general intelligence (AGI) in simple terms. The episode provides a fresh take on how to communicate complex topics to a lay audience, offering both clarity and insight.Additional materials: www.superdatascience.com/816Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

Polars, Python, Narwhals, Rust, and Pandas: Marco Gorelli talks to Jon Krohn about the many ways to use the newest data libraries available, the joys of open-source development, and the best method to win prizes in forecasting competitions.This episode is brought to you by AWS Inferentia and AWS Trainium, by Babbel, the science-backed language-learning platform, and by Gurobi, the Decision Intelligence Leader. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.In this episode you will learn:• When to use Polars vs Pandas [08:26]• How Polars optimizes string operations and data processing [20:08]• Where Narwhals outstrips Polars and Pandas [48:37]• The benefits of using Altair [55:21]• Addressing the lack of women in data science [1:09:58]• How to win a forecasting competition [1:16:58]Additional materials: www.superdatascience.com/815

As summer winds down, this episode shifts focus from the usual tech discussions to something more personal: reflecting on the importance of balancing work with life’s simple pleasures. While the world of data science and AI continues to evolve rapidly, it's essential to remember that true success isn't just about professional milestones. It’s also about cherishing the moments that make life meaningful. Tune in for a brief but impactful reflection on how to redefine success to include not just achievements, but also the everyday joys that often go unnoticed.Additional materials: www.superdatascience.com/814Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

Jerry Yurchisin from Gurobi joins Jon Krohn to break down mathematical optimization, showing why it often outshines machine learning for real-world challenges. Find out how innovations like NVIDIA’s latest CPUs are speeding up solutions to problems like the Traveling Salesman in seconds.Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.In this episode you will learn:• The Burrito Optimization Game and mathematical optimization use cases [03:36]• Key differences between machine learning and mathematical optimization [05:45]• How mathematical optimization is ideal for real-world constraints [13:50]• Gurobi’s APIs and the ease of integrating them [21:33]• How LLMs like GPT-4 can help with optimization problems [39:39]• Why integer variables are so complex to model [01:02:37]• NP-hard problems [01:11:01]• The history of optimization and its early applications [01:26:23]Additional materials: www.superdatascience.com/813

In this episode of Five-Minute Friday, Jon Krohn investigates published findings from the startup Sakana AI and its paper’s co-authors from the University of Oxford, the University of British Columbia and the Vector Institute in Toronto. These authors explore the potential of The AI Scientist, a framework that could change the way we conduct scientific research forever.Additional materials: www.superdatascience.com/812Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.