Abiodun Alagbada’s Post

𝗠𝗼𝘀𝘁 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗳𝗮𝗶𝗹 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗲𝘆 𝗹𝗮𝗰𝗸 𝗺𝗼𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻. They fail because they don’t have a clear direction. One day SQL. Next day Python. Then data analytics, AI, or another random course. A lot of effort. But no clear path. 𝗜𝗗𝗘𝗔 𝗴𝗶𝘃𝗲𝘀 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗵𝗲𝗹𝗽𝘀 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 𝗯𝘂𝗶𝗹𝗱 𝗰𝗮𝗿𝗲𝗲𝗿 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 𝘄𝗶𝘁𝗵: ✅ Real skills ✅ Real tools ✅ Real projects ✅ Practical guidance Because motivation lasts for days. Direction builds careers. 🎯 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗙𝗿𝗲𝗲 𝗗𝗲𝗺𝗼 𝗖𝗹𝗮𝘀𝘀 #DataAnalytics #DataEngineering #CareerGrowth #StudentLife #SkillDevelopment

We spent 4 years learning how to write a for loop. Meanwhile, AI agents are out here writing entire codebases while we're still debating whether to use a list or a tuple. Python basics aren't the problem. They're the foundation , and that's exactly it. A foundation was never meant to be the whole building. The curriculum hasn't quite caught up to the moment. And the moment is moving fast. Nobody's blaming anyone. It's just… interesting that "intro to programming" in 2026 looks a lot like it did in 2014. Same syntax. Same snake_case conventions. Same "here's how to print Hello World." The world graduated. The syllabus is still in the hallway. 🎓 If you're a student right now , you're not behind because of your college. You're behind if you only learn what college teaches you. Prompt engineering, agents, RAG pipelines, MCP servers, none of that is in the textbook yet. But all of it is in the job description. 👀 Learn the basics. Then immediately go further. The fax machine still works. Just nobody's waiting on the other end anymore. 📠 #Tech #Programming #Python #AI #MachineLearning #AIAgents #CareerAdvice #Developers #SoftwareEngineering #CSEducation #FutureOfWork #GenAI #LearnToCode #TechCareers #Students #CodingLife #PromptEngineering #AITools #BuildInPublic #TechTwitter

I just finished "Hands-On Programming with R", and it solidified a hard truth: In an AI-integrated world, learning SQL, R, and Python is no longer exclusive to Computer Science. These are now foundational tools for every career path. The business landscape is shifting. Those who learn to adapt and apply these tools will navigate the change; those who cling to the status quo risk being "naturally selected" out of the loop. Why R specifically? It’s more than a language for data; it’s a framework for strategic thinking. R forces you to stop looking at data as static rows and start seeing it as a dynamic system you can manipulate, simulate, and project. By stacking these technical skills onto our existing industry expertise, we don't just "keep up" with AI—we lead it. We become the architects of the prompts and the auditors of the outputs. Don't wait for the change to happen to you. Be the one who applies the code to the strategy. #RStats #DataScience #StrategicThinking #FutureOfWork #SkillStacking #Leadership #ContinuousLearning

🚀 𝐅𝐫𝐨𝐦 𝐀𝐛𝐚𝐜𝐮𝐬 𝐭𝐨 𝐀𝐈: 𝐖𝐡𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧 𝐈𝐬 𝐘𝐨𝐮𝐫 𝐒𝐦𝐚𝐫𝐭𝐞𝐬𝐭 𝐂𝐚𝐫𝐞𝐞𝐫 𝐌𝐨𝐯𝐞 𝐓𝐨𝐝𝐚𝐲 Every powerful technology we use today started with something simple. From the abacus to modern computers… From binary numbers to intelligent systems… From complex machine language to human-friendly programming like Python. That evolution tells one clear story: 👉 Technology rewards those who adapt early. 📘 While exploring Introduction to Computer Programming with Python, a few powerful insights stood out: 💡 Programming is not just coding — it’s problem-solving It begins with understanding problems, designing algorithms, and building solutions step by step. 💡 Computers think in logic, not magic Behind every app is a system of numbers, operations, and structured instructions working together. 💡 Python simplifies complexity With clean syntax and versatility, Python makes it easier for beginners to enter tech and for professionals to scale solutions. 💡 Strong fundamentals beat shortcuts Concepts like data types, loops, functions, and algorithms are the real game-changers in long-term growth. 💡 The future belongs to builders From data analytics to AI, Python is at the core of innovation across industries. 🎯 My takeaway: You don’t need to start big. You need to start right. Because in the world of tech, small consistent learning → massive long-term impact. If you're thinking about learning programming in 2026, start with Python… and start today. 👉🏻 follow Alisha Surabhi for more such content 👉🏻 PDF credit goes to the respected owners #Python #Programming #TechSkills #AI #CareerGrowth #Learning #DataScience

Why do projects matter more than certificates in 2026? In today’s competitive tech world, one thing is very clear: Certificates can get attention, but projects prove your skills. Many students focus on collecting certificates… But industry looks for: ✔️ Practical knowledge ✔️ Real-time problem-solving ✔️ Hands-on experience What separates a learner from a professional is the ability to apply knowledge in real-world scenarios. For example: Anyone can learn Python syntax… But can you - Analyse a real dataset? - Build a prediction model? - Explain your solution clearly? That's what truly matters. If you want to stand out in 2026: ✔️ Build real-time projects ✔️ Work on business problems ✔️ Create a strong portfolio ✔️ Practice consistently Because at the end of the day: "Your projects speak louder than your resume." Start building. Start learning by doing. #DataQuotes #DataQuotesEduTech #DataScience #AI #MachineLearning #Projects #CareerGrowth #Upskilling

Writing code that works is only the beginning. The real difference comes from writing code that works efficiently. The right data structures and algorithms help you build software that is faster, more reliable, and easier to maintain. They influence how applications handle large amounts of data, how websites respond under heavy traffic, and how AI models process information effectively. When you understand which structure to use; arrays, linked lists, trees, hash maps, queues, or graphs, your solutions become more predictable and scalable. Debugging becomes easier because your code is organized with intention and built to perform consistently. This is what separates simply writing code from thinking like an engineer. Strong foundations in data structures and algorithms improve every project you build and every technical problem you solve. Develop the skill that powers efficient software and professional-level problem-solving. Master data structures and algorithms with Learn Programming Academy and start building smarter code today. #programming #java #python #coding #LearnToCode

In 2026, the most effective teams don't choose between R and Python; they integrate. My latest article demonstrates how R serves as a sophisticated bridge, offering seamless Python interoperability and GPU-accelerated computing that fits into any production-level stack. I’ve compiled these workflows into a new Medium article, supported by the R-Cyclopedia: a GitHub repository containing a fully executed notebook designed to be a "Force Multiplier" for your technical stack. Why this matters across the board: - For Juniors: Move beyond "black-box" libraries. Learn Defensive Programming and how to architect Neural Networks from first principles. - For Seniors: Master the infrastructure of WSL2, CUDA, and {reticulate} to enable R-based research to deploy on high-performance GPU clusters. - For Academics: Master the full spectrum of R's capabilities: from lexical scoping and dynamic argument handling to scalable parallelization and GPU-accelerated workflows. The Highlight: A Keras/TensorFlow neural network trained on a GPU within R, managed via WSL2. This setup allows researchers to remain in their preferred statistical environment while leveraging the raw power of modern hardware. Whether you're looking to optimize a simulation or bridge the collaboration gap between R and Python teams, this guide is for you. Read the Full Article on Medium: https://lnkd.in/e2_Rb-V4 #R #Statistics #DataScience #Python #DeepLearning #GPUComputing #PhD #SoftwareEngineering #WSL2 #MachineLearning #RServer #Parallelization #Vectorization #Iterations #Nvidia #GPU #Ubuntu

📌 I realized something today… Even after working in a technical environment, going back to fundamentals can unlock a whole new level of clarity. Today, I focused on strengthening my Python basics — and honestly, it changed how I see simple operations. 💡 As someone already working in a technical role, I’m now consciously revisiting core concepts to build a stronger foundation for advanced skills. 🔍 What I learned today: • File handling in Python (reading, writing, appending) • Using with open() for cleaner and safer code • Understanding file pointer methods like seek() and tell() • Writing efficient code using lambda functions • Using map, filter, and reduce for data processing • The critical difference between is and == ⚡ Key Takeaways: • Clean code matters — with open() is a small change with big impact • Lambda functions simplify logic when used correctly • map, filter, and reduce make data handling powerful and elegant • Understanding memory vs value (is vs ==) prevents subtle bugs 🌍 Real-World Relevance: These concepts are not just theoretical — they are used in: Data processing pipelines Automation scripts Backend systems Web scraping projects As I continue this journey, I’m realizing: 👉 Strong fundamentals = Faster growth in advanced tech skills 💬 Question for you: Do you revisit fundamentals after gaining experience, or focus only on advanced topics? 🔗 Let’s connect and grow together! Follow me for more learning updates. #Python #WebDevelopment #LearningJourney #Coding #100DaysOfCode #CareerGrowth #Programming #PythonBasics #SelfImprovement

🚀 Completed Day 3 of My RAG (Retrieval-Augmented Generation) Learning Journey Just wrapped up Day 3 of my hands-on learning using a practical implementation video, and today was all about bridging theory with real-world execution. 🔍 Key takeaways from today’s session: Setting up the Python environment and resolving real-world setup issues Creating and managing virtual environments (venv) for isolated development Understanding how an ingestion pipeline works in a RAG system Structuring project files and preparing data for downstream retrieval One thing that stood out — building RAG systems isn’t just about understanding LLMs, but about how efficiently you handle data pipelines, dependencies, and environment configuration. Faced a few setup challenges (like environment activation and package installation), but solving them gave deeper clarity into how real development workflows operate. Looking forward to exploring embeddings, vector databases, and retrieval optimization in the next phase. link for video :- https://lnkd.in/dvYW59Kh #RAG #ArtificialIntelligence #MachineLearning #GenerativeAI #LLM #Python #DataEngineering #MLOps #AIEngineering #TechLearning #LearningInPublic #BuildInPublic #OpenToWork #CareerGrowth #TechCommunity #Innovation #FutureOfAI

🚀 I learned Functional Programming in Python — As an M.Sc. Computer Science student, I’ve been exploring new concepts daily, and today I dived into Functional Programming. 💡 What is Functional Programming? It’s a programming style where we write code using functions, avoid changing data, and focus on “what to do” rather than “how to do it.” 🔹 Key Concepts: ✔️ Pure Functions – Same input → Same output ✔️ Immutability – Data is not modified ✔️ Higher-Order Functions – Functions that take other functions as input 🧠 Simple Python Example: Using built-in functions like map(), filter(), and reduce() 👉 Example: map() → applies a function to all elements filter() → selects elements based on condition 🎯 Why it matters? Cleaner and more readable code Easier debugging Widely used in modern technologies (Data Science, AI/ML) 📌 Learning this helped me understand how to write more efficient and structured code. I’m currently exploring more concepts in Python, AI, and Machine Learning. 💬 If you’re learning too, let’s connect and grow together! #Python #FunctionalProgramming #Coding #AI #MachineLearning #ComputerScience #LearningJourney #TechStudents