Data 2 Pattern

🚀 Advance Your Career in Quantitative Finance! Applications for the WorldQuant University (WQU) Master of Science in Financial Engineering (MScFE) program are officially open. Whether you're looking to break into quantitative trading, risk management, data science, or financial analytics, this rigorous, practitioner-focused program covers everything from computational finance to advanced modeling. 📋 Admission & Transcript Requirements: * Bachelor's Degree: A completed undergraduate degree from a recognized institution. * Transcripts: * Application: You can upload a scanned copy of your transcript for your highest earned degree. * Enrollment/Acceptance: Official transcripts sent directly from your former college or university (via email or postal mail) are required upon acceptance (must be submitted by the end of your second course). * Quantitative Proficiency Test: Pass with a score of 75% or higher (covers math, stats, and basic Python). * English Proficiency: Required if your native language is not English or if your prior degree wasn't taught in English (TOEFL, IELTS, Duolingo, or PTE accepted). * Government-Issued Photo ID: Passport, national ID, or driver's license. 💡 Why WQU MScFE? * 100% Tuition-Free: Access high-tier quant education without traditional university overhead. * Flexible & Online: Designed for working professionals to study from anywhere in the world. * Industry-Relevant Curriculum: Master Python, R, stochastic calculus, machine learning, and portfolio optimization. Ready to take the next step in your quant journey? 👉 Apply here: https://www.wqu.edu/mscfe-apply #QuantFinance #FinancialEngineering #MachineLearning #Python #DataScience #CareerGrowth

What I've Been Learning Over the Last Two Days For the past two days, I've been diving into one of the most fascinating topics in backend engineering: how databases physically store and retrieve data. Like many developers, I've spent years writing SQL queries, creating indexes, and building applications on top of databases. But I realized I didn't fully understand what actually happens after executing an INSERT, UPDATE, or SELECT. So, I decided to go deeper. I've been learning about: - How data is stored on disk - B-Trees and LSM Trees - Write-Ahead Logs (WAL) - MemTables and SSTables - Storage engines and indexing - The trade-offs behind different database designs One thing that has made learning much easier is combining reading with visual simulations. Seeing these concepts animated while reading about them has helped me build a much stronger mental model of how databases work internally. Resources I'm using: 📖 Learning resource based on Designing Data-Intensive Applications (DDIA) concepts https://designing-data-intensive-applicatio.vercel.app/ 🎥 Database Storage Explained (visual simulation) https://youtu.be/TIkSh6teQFo I'm not trying to memorize every detail. My goal is to understand the why behind database design decisions so I can become a better backend engineer and system designer. The more I learn, the more I appreciate the incredible engineering behind systems like PostgreSQL, MySQL, RocksDB, Cassandra, and LevelDB. Still learning. Still curious. One concept at a time. I used the book "Designing Data-Intensive Applications" as a resource. ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: @data_to_pattern @data_to_pattern ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: #BackendEngineering #Database #SystemDesign #DataEngineering #ComputerScience #DDIA #PostgreSQL #StorageEngine #LearningInPublic

guys here is the link you can join live with this link please time 9:00 or 3pm EAT https://www.youtube.com/live/VFj5gV1k6SQ?si=eRnnmF3xuiBD0Wfs and please here is day 1 code link https://www.kaggle.com/code/yisberh/day-1-python-basics please everyone create kaggle.com acount for this course

📚 Designing Data-Intensive Applications — Chapter 4: Encoding & Evolution Imagine your application has been running for years. Then you introduce a new feature and change the data schema. But the old version of your application is still running, and your database still contains old data. How can we change the system without breaking what already exists? This is the main problem Chapter 4 addresses: evolution and compatibility. When data moves between processes or is stored on disk, it needs to be encoded into bytes and later decoded. As systems evolve, we need - backward compatibility—new code can read old data - forward compatibility—old code can read new data. The chapter explores different encoding formats such as JSON, XML, CSV, Thrift, Protocol Buffers, and Avro, and then follows how data flows through databases, REST/RPC services, and asynchronous message brokers. 💡 Key lesson: Code changes quickly, but data lives much longer. Therefore, we should design our data formats and APIs so that our systems can evolve safely. 📖 Short Notes: Chapter 4 — Encoding & Evolution Notes 🎥 YouTube: Chapter 4 — Encoding and Evolution #DDIA #SystemDesign #DistributedSystems #DataEngineering #BackendEngineering

🚀 Free AI Course From Zero — Python to Deep Learning Hello everyone! I am planning to start a free 2-month AI course for beginners. This course will start from basic Python and slowly move into Machine Learning, Deep Learning, Generative AI, and real AI projects. But to begin this class, I need at least 100 serious students. So please, if you are interested, join and also share this message with at least 5 people who may want to learn AI. 📌 Course Plan ✅ 1. Basic Python Variables Conditions Loops Functions Lists and dictionaries Beginner Python projects ✅ 2. Python for Data Science NumPy Pandas Data cleaning Data visualization Working with CSV files ✅ 3. Math for AI Basic statistics Probability Vectors and matrices Gradient descent idea ✅ 4. Machine Learning Linear regression Logistic regression Decision trees Random forest Model evaluation Real ML projects ✅ 5. Deep Learning Neural networks Weights and bias Activation functions Loss function Backpropagation Image classification project ✅ 6. Generative AI Prompt engineering RAG basics AI agents basics Simple AI chatbot project 🎥 How the course will work Lessons will be uploaded on one YouTube channel Code will be shared for every video We will have live teaching sessions We will have live Q&A We will have group discussion and support The course duration is only 2 months 💰 Important Note The main course is free. For students who need personal support, project review, career guidance, or one-on-one help, there will also be a paid mentorship option. 🎯 Who can join? Complete beginners Students Anyone interested in AI Anyone who wants to build real AI projects Anyone who wants to start learning Python, ML, and Deep Learning 📢 Please help us reach 100 students so we can start the class. If you are interested, reply with: “I want to join” And please share this message with at least 5 friends. Let’s build a strong AI learning community together! 🔥 it start from next week june 15 2026

Free Beginner Machine Learning Full Course for Ethiopians! 🇪🇹 Hello Ethiopian Data Science & Machine Learning Community, We are excited to announce a complete beginner-friendly Machine Learning course designed for anyone who wants to start learning ML from zero. This course is for you if you are: ✅ New to Machine Learning ✅ Interested in Data Science and AI ✅ A student or self-learner ✅ Looking for practical, step-by-step lessons ✅ Want to build real ML projects We will cover: 📌 Python basics for ML 📌 Data cleaning and preprocessing 📌 Supervised and unsupervised learning 📌 Model training and evaluation 📌 Real-world ML projects 📌 How to continue learning AI and Data Science No advanced background is required. We will start from the basics and build everything step by step. Let’s learn together and grow the Ethiopian AI and Data Science community. 🇪🇹🤖 📢 Join us and share this with anyone interested in Machine Learning! also you can find more videos here https://www.youtube.com/@yisakbule please sub it

https://www.youtube.com/watch?v=gPmzrp6c3D4

🐳 Spent some time this week actually digging into how Docker works under the hood, not just "docker run" and move on. Sharing what clicked for me. The biggest realization: a container isn't really a "thing." It's just a normal Linux process that's been boxed in using two kernel features — namespaces and cgroups. Namespaces control what the process can see (its own PIDs, network, filesystem, hostname). Cgroups control what it's allowed to use (CPU, memory, I/O). That's it. No magic container object in the kernel — Docker is basically a really good abstraction layer on top of stuff that's been in Linux since 2002. Also didn't realize how many layers are actually involved when you type docker run. The CLI talks to dockerd, which hands things off to containerd, which hands things off to runc, which is the thing that finally talks to the kernel and sets up the namespaces/cgroups. Docker itself is more of a coordinator than the thing doing the heavy lifting. And the image layering thing finally makes sense to me now — every line in your Dockerfile is a read-only layer, and when you run a container it just adds one thin writable layer on top. That's why spinning up a container is fast — you're not copying a whole filesystem, just stacking diffs (copy-on-write). Anyway, if you want the full deep dive (fair warning, it's long — 35 min read, but worth it), here's the article that walked me through it: https://medium.com/@furkan.turkal/how-does-docker-actually-work-the-hard-way-a-technical-deep-diving-c5b8ea2f0422