How to make data science projects production ready with Khuyen Tran

Found this article interesting when architecting RAG Systems. Six Lessons Learned Building RAG Systems in Production | Towards Data Science https://lnkd.in/g8tP7pug

Hey everyone 👋 This page is about practical data science. I’ll be sharing short summaries from real world data science work - the problems we think through, the approaches we try, what works, what doesn’t, and the learnings behind each decision. No theory dumps. No hype. Just practical logs from day-to-day data science, along with relevant tech updates when they matter. If that sounds useful, feel free to follow along.

On my whole Data Science experience (apart from being an academic requirement) I had made up an article about why Learning Rate is the "Ace of Training Parameters"! Just make sure to carefully pick up the right choice of optimizers though ;). https://lnkd.in/gc6PgeEk

We’re closing our five-part Model Biases series with a quiet troublemaker every data scientist knows too well: endogeneity. In this final installment, Ted Lorenzen, our Director of Data Science, breaks down how endogenous relationships slip into time-series systems, warp causal signals, and challenge even the most careful modeling workflows. Whether you’re building the models or relying on their outputs, this is the finale worth your attention. Read the full blog here: https://hubs.ly/Q03XYFrR0

02. Data science Life cycle | 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 (Part 2/3) #DataScience #LearningSeries #ModuleBased #ScriptWithSaad #PythonForDataScience #MachineLearning #Roadmap ✍️ By: Script With Saad

How to Structure Your Data Science Project in 2026? Ever felt lost in messy folders, so many scripts, and unorganized code? That chaos only slows you down and hardens the data science journey. Organized workflows and project structures are not just nice-to-have, because it affects the reproducibility, collaboration and understanding of what’s happening in the project. In this blog, we’ll explore the best practices plus look at a sample project to guide your forthcoming projects....

Understanding eigenvalues and eigenvectors is key to mastering linear algebra and data science. Learn how to calculate them and why they matter! Watch the full video here: https://lnkd.in/dhxmW8tu

Will LLM-based Agents replace Data Scientists? Check out our new paper exploring and reviewing how agents are being used for data analytics and visualization: https://lnkd.in/dGgFJRQV Mafalda Martins Miguel de Castro Neto André Barriguinha

🌟 Metadata: the star on top of your data tree 🌟 Just like the star completes the tree, good metadata completes your data. Without it, even the most beautiful dataset can get lost in the forest. On the 8th day of Open Science Advent, we're celebrating metadata and data documentation - the finishing touches that bring clarity, structure, and meaning to your data. Whether you're preparing datasets and code for sharing, ensuring reproducibility or helping others (and future you) navigate your research, thoughtful documentation makes all the difference. Check out the e-learning module on Data Documentation and Metadata to ensure your metadata shines bright: https://lnkd.in/ei7RUZCn #OpenScienceAdvent #DataDocumentation #Metadata

80% of data science is data cleaning. Do it faster. If you’re spending hours wrangling data, you’re doing it wrong. I’ve distilled the most powerful Pandas functions into one scannable sheet. From .loc to .apply(), everything you need is right here.  Save this post so you never lose your flow state again.