Ironhack Germany’s Post

Hi all, In today's world, it is absolutely essential for data scientists to learn and master software engineering skills and principles to deploy ML models in production. Here is a technical cheatsheet I formulated accumulating all software engineering tools to learn for data scientists, based on my experience and I learnt from the book Catherine Nelson 'Software engineering for data scientists'. Hope it serves as a guidance. #softwareengineeringfordatascientists #catherinenelson #mlops

"Unlock the power of sorting algorithms! From the simplicity of insertion sort to the speed of quick sort, understanding sorting algorithms is key to optimizing performance in computer science and beyond. Dive into the world of sorting . Whether you're a seasoned developer or a curious learner, uncovering the secrets of sorting algorithms promises to elevate your problem-solving skills and revolutionize your approach to data organization." ✨🎗️ #SortingAlgorithms #algorithmoptimization #efficiency #TechTips #Programming101 #DataOrganization #ProblemSolvingSkills #CodingCommunity #TechInsights

A few weeks ago, I had a conversation with a SWE friend of mine about algorithmic complexity in ML vs software development. This discussion, however piqued my curiosity about its validity and as I dug deeper, I realized the importance Big O notation but I couldn't find a lot of good examples to showcase it. I also found the explanations to be too complex, thus, I've also tried to explain simply some of the most common Big O notations: Here's an excerpt from the blog of simplifying the notations : O(n): Linear Time What it means: The running time increases directly with the amount of data. Example: Reading a list of names. If the list doubles in size, it will take twice as long to read through it. O(n log n): Linearithmic Time What it means: The running time grows a bit faster than linear time, but not as fast as quadratic time. Example: Sorting a deck of cards. Sorting a larger deck takes longer, but efficient methods make the increase manageable. O(n²): Quadratic Time What it means: The running time increases much faster as the amount of data grows. Example: Comparing every student with every other student in a class to find the tallest pair. Doubling the number of students makes the task take four times as long. Check out the full article for more and let me know your thoughts! #DataScience #MachineLearning #BigONotation #AlgorithmicComplexity #Coding

🚀 Mastering Data Science: Dive into the world of Data Science with a blend of technical insights, hands-on practical examples, and core mathematical concepts. Whether you're coding, analyzing, or solving complex problems, this journey will empower you to unlock the true potential of data! 📊🔍✨ #DataScience #LearningJourney #Tech #Mathematics #LinearRegression

Turning coffee into code, one algorithm at a time. Living the dream as a machine learning engineer: where bugs are features and data is the ultimate truth. #cliqcloud #cliqcloudmemes 💻🤖 #MachineLearningLife #CodeAndCoffee

💡 Did you know? Around 87% of machine learning projects never make it to production. 🚀 This high failure rate often results from a lack of alignment between technical goals and real-world business needs. As ML engineers, bridging the gap between data science and business applications is key to driving real impact. 📊🤝 This fact underscores the importance of practical application in ML projects, making it a great conversation starter on LinkedIn!

How will the next evolution of AI impact software development? Planview’s Chief Data Scientist, Dr. Richard Sonnenblick, explains what you need to know. https://okt.to/sgP4cq

Yes, we’ll be there at the 10th Craft Conference this May. But why? 👋 Craft is great for sharing first-hand experiences. We are poised to show best practices in data engineering and AI with our engineers at the DATAPAO booth. 🎓 The Conference is the right place for learning about the latest trends in software engineering - and how we could contribute to them. 👷♂️ It’s also about people. Hundreds of software engineers gather for 2 days to share their experiences and learn from some of the biggest names in the industry. 🤝 And finally, we’d love to give back to our engineering community at an event that grew out to be one of the biggest conferences in the region - that’s why we also prepare a surprise challenge to anyone popping up at our booth. And remember: with our promo code, you’ll get 20% off your ticket - the promo link is in the 1st comment 👇 #dataengineer #dataengineering #dataengineers #datascience #datascientists #ai

Day 22 of the #30DaysDSAChallenge: Sorting Algorithms Unleashed! 🚀** Hello LinkedIn community! 👋 Today, on my 22nd day of the 30-day Data Structures and Algorithms Challenge, I immersed myself in the world of sorting algorithms. 📊 🌟 Algorithm Trio: - Count Sort: Efficient for small integer ranges, Count Sort counts occurrences and arranges elements accordingly. - Bucket Sort: Distributes elements into buckets, then sorts each bucket individually. - Radix Sort: Processes digits to sort integers, making it ideal for large datasets. 🚀 Reflections: Exploring these algorithms felt like unlocking secret codes. Their unique approaches—whether counting, bucketing, or digit-based sorting—have broadened my problem-solving toolkit. 🌟 Next Steps: I'm excited to apply these techniques in real-world scenarios and continue my algorithmic adventure. Stay tuned for more insights! 💡 Thank you for being part of this journey. Let's sort our way to success! 🌐 #SortingAlgorithms #DataStructures #CodingJourney #TechEnthusiast --- Feel free to customize this post further with your personal experiences or any additional thoughts. Keep up the great work, and let's keep the algorithms flowing! 🌟

This is everything someone needs to know about Machine Learning Feature Engineering. I have put together a super list of 'Feature Engineering' techniques and a typical enterprise-scale workflow. By the way, there is a dedicated branch of study in Engineering (Data Science) just for teaching these techniques. The entire data pipeline is built based on my understanding and visualization... it is debatable though.