Python experts Jodie Burchell, Carol Willing, and Paul Everett discuss Python's future trends, large language models' impact on science, transparency in AI, Python evolution, refactoring benefits, community inclusivity, data science challenges, and the importance of human creativity in programming.

Jodie Burchell, a developer advocate for data science at JetBrains with a PhD in clinical psychology, shares her expertise on sentiment analysis. She discusses traditional lexicon-based methods and advanced machine learning techniques, highlighting the evolution of sentiment analysis with large language models. Jodie emphasizes the challenges of linguistic nuances and context in emotional classification. From practical applications in blogging to tools for analysis like TextBlob, she provides valuable insights for anyone looking to dive deeper into this field.

Jodie Burchell, developer advocate for data science at JetBrains, discusses techniques and tools for evaluating large language models (LLMs) using Python. They explore measuring bias, toxicity, and truthfulness in LLMs, the challenges and limitations of AI language models, the role of Python packages like Hugging Face, and the concept of grouping and acronyms. Jodie also shares benchmarking datasets and resources available on Hugging Face for evaluating LLMs.

The concept of literate programming, or the idea of programming in a document, was first introduced in 1984 by Donald Knuth. And as of today, notebooks are now the defacto tool for doing data science work. So as the data tooling space continues to evolve at breakneck speed, what are the possible directions the data science notebook can take? In this episode of DataFramed, we talk with Dr. Jodie Burchell, Data Science Developer Advocate at JetBrains, to find out how data science notebooks evolved into what they are today, what her predictions are for the future of notebooks and data science, and how generative AI will impact data teams going forward. Jodie completed a Ph.D. in clinical psychology and a postdoc in biostatistics before transitioning into data science. She has since worked for 7 years as a data scientist, developing products ranging from recommendation systems to audience profiling. She is also a prolific content creator in the data science community.Throughout the episode, Jodie discusses the evolution of data science notebooks over the last few years, noting how the move to remote-based notebooks has allowed for the seamless development of more complex models straight from the notebook environment.Jodie and Adel’s conversation also covers tooling challenges that have led to modern IDEs and notebooks, with Jodie highlighting the importance of good database tooling and visibility. She shares how data science notebooks have evolved to help democratize data for the wider organization, the tradeoffs between engineering-led approaches to tooling compared to data science approaches, what generative AI means for the data profession, her predictions for data science, and more.Tune in to this episode to learn more about the evolution of data science notebooks and the challenges and opportunities facing the data science community today.Links to mentioned in the show:DataCamp Workspace: An-in Browser Notebook IDEJetBrains' DataloreNick Cave on ChatGPT song lyrics imitating his styleGitHub Copilot More on the topic:The Past, Present, And Future of The Data Science NotebookHow to Use Jupyter Notebooks: The Ultimate Guide

Join Jodie Burchell, Maria Jose Molina-Contreras, and Jessica Greene as they discuss recent data science topics like model evaluations, practicality vs. complexity, bias assessment, and measuring metrics. They share insights on career transitions, challenges in the field, and the intersection of data science with other domains. The conversation covers a wide range of data science aspects and emphasizes the importance of networking and continuous learning.