Great to see Nathan Pollet, Lead Data Scientist in our CATIA Generative Experience Organization, featured in this post. A nice overview of what the role really looks like, from working with data to building Industry World Models that support our products and enable real customer engineering scenarios. Congrats! #WeAre3DS #DataScience #AI #GenerativeAI #Teamwork

Data shows you what happened. Analytics tells you why. Data science helps you predict what is to happen next. Now when one master all three , then that is what turns data into real impact. #DataScience #DataAnalytics #MachineLearning #AI #LearningJourney #DataThinking

Always create a simple baseline model before trying complex algorithms. For example, start with linear regression or logistic regression. This helps you understand whether your complex model is actually improving performance or not. Many beginners skip this step and jump straight to advanced models. A good data scientist always compares against a baseline first. #DataScience #MachineLearning #AI #DataAnalytics #LearningDataScience

Stop trying to master everything at once! 🛑 Switching gears between Data Engineering and AI while starting out is the fastest way to burn out. Focus on one, master it, then scale. #dataengineering #ai #machinelearning #careeradvice #techlearning #datascience #softwareengineering #DataEngineering #ArtificialIntelligence #CareerAdvice #MachineLearning #TechTips #LearningPath #DataScience

Data analytics is no longer just about dashboards and reports 📊. We're living in the age of AI, where data-driven decision making is more crucial than ever. As I delve into the world of AI and data science, I've learned a few key things about the future of decision intelligence: 1. AI agents are revolutionizing the way we analyze data 2. Human-centered analytics is becoming a top priority 3. Data foundations are the backbone of any successful AI strategy Check out this insightful article from Towards Data Science to learn more: https://lnkd.in/gpCKwWj6 What's your take on the future of data analytics in the age of AI? 💡 #AI #DataScience #DecisionIntelligence

My latest Learning Progress Review at Digital Skola brought me back to the fundamentals of Data Science and AI. From EDA and data preprocessing to basic machine learning concepts, I revisited how important it is to truly understand the data before building any model. It’s not just about running algorithms—it's about making sure the data is clean, meaningful, and ready to reveal hidden insights. At the end of the day, data isn’t just numbers—it tells a story that helps us make better decisions. #DigitalSkola #LearningProgressReview #DataScience

Data Science Briefing #311 Our weekly rundown of the latest news, tutorials and blog posts on AI, ML, and Data Science https://lnkd.in/e7cW7bNv

I started seriously using AI about a year ago, during my MSc, and what I found changed how I think about work entirely. Not enterprise AI. Not research papers. The kind of AI that helps a sole trader look like they have a full team behind them. And almost immediately, I started hearing the same two things from business owners: "I'm not technical enough." "I don't trust it with my data." Both are completely understandable. Both are also solvable. And not in the way most people think. My MSc was in AI & Data Science at Keele University. But the most useful thing I came away with wasn't technical. It was how to explain this stuff to people who don’t care how it works. Only whether it works for them. Something is coming on Friday. #AIImplementation #MSc #SmallBusiness

What Data Science and Analysis Is Teaching Me "In data science, the goal is not just prediction....it is understanding." Models may produce numbers, but data scientists create meaning from those numbers. And that’s where the real value lies. #DataScience #MachineLearning #AI #DataMatters #Analytics

Most companies don’t actually need more AI. They need better data science thinking. I’ve seen teams jump straight into Gen AI, LLMs, and fancy models, but skip the basics: • Is the data reliable? • Does the model solve a real business problem? • Can it actually run in production? Sometimes a simple well-designed ML pipeline creates more impact than a complicated AI system. Good data science isn’t about using the most advanced model. It’s about turning messy data into decisions that scale. Curious to hear from others working in data science: What created the biggest real-world impact for you, a complex model or a simple solution done right? #DataScience #MachineLearning #GenAI #AIinProduction