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....

If you missed this live AMA, it’s worth the watch. Ole Olesen-Bagneux and Abhay Singh break down what metadata really means—and why it’s the backbone of modern data architecture.

If you want to start a career in Data Science: Don't chase every tool. Focus on foundations first. → Learn SQL properly → Understand data cleaning → Practice asking the right questions Tools change. Thinking doesn't. Start with clarity, not complexity 🧠 #DataScience #CareerAdvice #MCA #TechCareer #LearningPath

𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐂𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 on designing production-ready forecasting pipelines, taught by Rami Krispin on DataCamp. 𝐀 𝐟𝐞𝐰 𝐤𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐬𝐨 𝐟𝐚𝐫: the course offers an easy-to-understand approach to integrating API function calls into real workflows, and it introduces a variety of forecasting models to experiment with and compare. The content is well-suited for beginner-level data scientists while also being valuable for experienced practitioners who want a structured refresher on forecasting pipelines.

Idempotency? Ever had to rerun a data job because something failed… and suddenly your tables are full of duplicates? 😅 That’s why idempotency matters!!! Idempotency means this: ->Run the pipeline once ->Run it twice ->Run it ten times =>You still get the same clean result =>No duplicate dimensions =>No inflated facts =>No misleading dashboards In real-world data engineering, reruns are unavoidable, but bad loads are!! Make your pipelines idempotent — to make recovery routine, not a disaster. 🚀 #DataEngineering #Idempotency #ETL #DataQuality #DataWarehouse

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

I'm always thinking 🤔 and the results of today's thinking has yielded you an early Xmas present 🎁 😮 Metadata not only exists to describe data, it also describes your (ETL) processes, some engines already store some of it inherently (usually package names, runtime history, logs etc), but with a bit of effort you can embellish it with even more! Think about things like dependencies, frequency of execution, parameters etc You can store it anywhere that is programmatically accessible, files, database table etc. Why you ask? You are going to need it!! #metadata #agenticai #agentorchestration #youcantdoanythingdecentwithouthavinggooddata

A big shout-out to Shannon Barrow and Kyle Hale for this clear, myth-busting Databricks blog on Lakehouse data modelling. It addresses the exact questions I keep hearing from architects and engineers: relational modelling, keys and constraints, semantic modelling, BI performance, medallion, and more. I turned the key takeaways into a quick, friendly infographic for my network in this post 📄✨ Read the original blog here: https://lnkd.in/eBJs_s9Y If you are modernising a warehouse or designing governed analytics on the Lakehouse, this is worth a look. What myth do you still hear most often in your organisation? ♻️ Repost if you think this would help your team or network. ➕ Follow me Lingeshwaran Kanniappan for more! #Databricks #Lakehouse #DataModeling #DeltaLake #UnityCatalog #Analytics #DataEngineering

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