Eric J Ma's Website

In this blog post, I share how I've completely revamped The Data Science Bootstrap Notes for 2025, reflecting major changes in Python tooling and best practices. I discuss moving from conda to pixi and uv, automating project setup with pyds-cli, integrating AI thoughtfully, and embracing CI/CD for reproducible workflows. I also highlight the core philosophies that guide my approach and explain what outdated advice I've removed. Curious how these changes can help you build scalable, modern data science projects?

In this blog post, I share 10 practical ways I've used AI and large language models to save time and boost my effectiveness at work—beyond just coding and emails. From crafting tailored presentations and prepping for negotiations to automating tedious forms and practicing tough conversations, these strategies help you focus on what really matters. Want to know how AI can help you work smarter, not harder, beyond 2025?

In this blog post, I share practical strategies for data scientists and statisticians to communicate more effectively with lab scientists in biotech. Instead of overwhelming collaborators with methods, I explain how to focus on decision-making, translate complex analyses into actionable probabilities, and build trust through clarity. I also offer tips for structuring meetings and anticipating common questions. Want to know how to make your insights drive real decisions in the lab?

In this blog post, I share how I discovered a powerful Python trick: dynamically changing a function's source code at runtime using the compile and exec functions. This technique enabled me to build more flexible AI bots, like ToolBot, that can generate and execute code with access to the current environment. While this opens up exciting possibilities for LLM-powered agents and generative UIs, it also raises serious security concerns. Curious how this hack can supercharge your AI projects—and what risks you should watch out for?

In my latest post, I share how large language models are changing the data science landscape—not by replacing us, but by making us more effective and opening up new opportunities to build custom AI solutions. I discuss why our skills in measurement and evaluation are more valuable than ever. Curious how data scientists can thrive in the LLM era?

In this blog post, I share my journey exploring the R2D2 framework for Bayesian modeling, which lets you intuitively control model fit by placing a prior on R² instead of individual coefficients. I walk through its elegant extensions to generalized linear and multilevel models, showing how it automatically allocates explained variance and prevents overfitting. Curious how this approach can simplify your modeling and highlight the most important factors in your data?

In this blog post, I share how months of hands-on struggle and learning paved the way for me to ship a complex graph-based memory feature for Llamabot in just two days—with AI as my design partner. I explain why you have to "earn your automation" and how AI can amplify, not replace, your critical thinking. Curious how pairing deep preparation with AI can supercharge your workflow and lead to breakthroughs?

In this blog post, I share how using xarray can transform laboratory and machine learning data management by unifying everything—measurements, features, model outputs, and splits—into a single, coordinate-aligned dataset. This approach eliminates the hassle of index-matching across multiple files, reduces errors, and makes your workflow more reproducible and cloud-ready. Curious how this unified structure can simplify your experimental data analysis and save you time? Read on to find out!

In this blog post, I reflect on my 10th year at the SciPy Conference, sharing highlights from teaching tutorials, attending inspiring talks, recording informal podcast conversations, and contributing to open source projects. I discuss the power of community, the evolution of scientific notebooks, and the importance of financial aid in making SciPy accessible. Curious about the behind-the-scenes moments and lessons learned from a decade at SciPy?

In this blog post, I reflect on the challenges of integrating AI into education and the workplace, sharing lessons from educators who found that unrestricted AI access can undermine true learning and assessment. I discuss why it's crucial to earn the privilege to use automation by first mastering foundational skills and demonstrating the ability to verify AI outputs. How can we ensure that AI enhances, rather than replaces, our critical thinking and problem-solving abilities?