Technical Skill Development

I spent 3+ hours in the last 2 weeks putting together this no-nonsense curriculum so you can break into AI as a software engineer in 2025. This post (plus flowchart) gives you the latest AI trends, core skills, and tool stack you’ll need. I want to see how you use this to level up. Save it, share it, and take action. ➦ 1. LLMs (Large Language Models) This is the core of almost every AI product right now. think ChatGPT, Claude, Gemini. To be valuable here, you need to: →Design great prompts (zero-shot, CoT, role-based) →Fine-tune models (LoRA, QLoRA, PEFT, this is how you adapt LLMs for your use case) →Understand embeddings for smarter search and context →Master function calling (hooking models up to tools/APIs in your stack) →Handle hallucinations (trust me, this is a must in prod) Tools: OpenAI GPT-4o, Claude, Gemini, Hugging Face Transformers, Cohere ➦ 2. RAG (Retrieval-Augmented Generation) This is the backbone of every AI assistant/chatbot that needs to answer questions with real data (not just model memory). Key skills: -Chunking & indexing docs for vector DBs -Building smart search/retrieval pipelines -Injecting context on the fly (dynamic context) -Multi-source data retrieval (APIs, files, web scraping) -Prompt engineering for grounded, truthful responses Tools: FAISS, Pinecone, LangChain, Weaviate, ChromaDB, Haystack ➦ 3. Agentic AI & AI Agents Forget single bots. The future is teams of agents coordinating to get stuff done, think automated research, scheduling, or workflows. What to learn: -Agent design (planner/executor/researcher roles) -Long-term memory (episodic, context tracking) -Multi-agent communication & messaging -Feedback loops (self-improvement, error handling) -Tool orchestration (using APIs, CRMs, plugins) Tools: CrewAI, LangGraph, AgentOps, FlowiseAI, Superagent, ReAct Framework ➦ 4. AI Engineer You need to be able to ship, not just prototype. Get good at: -Designing & orchestrating AI workflows (combine LLMs + tools + memory) -Deploying models and managing versions -Securing API access & gateway management -CI/CD for AI (test, deploy, monitor) -Cost and latency optimization in prod -Responsible AI (privacy, explainability, fairness) Tools: Docker, FastAPI, Hugging Face Hub, Vercel, LangSmith, OpenAI API, Cloudflare Workers, GitHub Copilot ➦ 5. ML Engineer Old-school but essential. AI teams always need: -Data cleaning & feature engineering -Classical ML (XGBoost, SVM, Trees) -Deep learning (TensorFlow, PyTorch) -Model evaluation & cross-validation -Hyperparameter optimization -MLOps (tracking, deployment, experiment logging) -Scaling on cloud Tools: scikit-learn, TensorFlow, PyTorch, MLflow, Vertex AI, Apache Airflow, DVC, Kubeflow

Here are 20 advanced analytics concepts every analyst should know: Most analysts learn SQL and dashboards, then stop there. But the ones who get promoted? They think in frameworks. They know how to break down problems, find root causes, and connect metrics to business decisions. ����𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞: 𝐰𝐡𝐚𝐭'𝐬 𝐡𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠 ↳ Cohort Analysis ↳ Funnel Analysis ↳ Retention Analysis ↳ Customer Segmentation ↳ RFM Analysis ↳ Metric Trees ↳ KPI Design 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜: 𝐰𝐡𝐲 𝐢𝐭'𝐬 𝐡𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠 ↳ Churn Analysis ↳ Attribution Modeling ↳ A/B Testing ↳ Hypothesis Testing ↳ Regression Analysis ↳ Anomaly Detection ↳ Root Cause Analysis 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞: 𝐰𝐡𝐚𝐭'𝐬 𝐥𝐢𝐤𝐞𝐥𝐲 𝐭𝐨 𝐡𝐚𝐩𝐩𝐞𝐧 𝐧𝐞𝐱𝐭 ↳ Lifetime Value Analysis ↳ Time Series Analysis ↳ Forecasting ↳ Predictive Analytics 𝐏𝐫𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞: 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐬𝐡𝐨𝐮𝐥𝐝 𝐝𝐨 𝐚𝐛𝐨𝐮𝐭 𝐢𝐭 ↳ Decision Intelligence ↳ Prescriptive Analytics Notice the pattern. Descriptive and diagnostic have the most tools, because that's where most teams operate. Predictive and prescriptive are thinner, and that's exactly why they stand out. If you can only work the first two columns, you're a reporting analyst. If you can move through all four on the same problem, you're the one who gets promoted. Save this as a reference for where to focus next. ♻️ Repost if this is useful for your network.

If you're in tech, Python is a skill that can take you far. But where do you start, and how do you progress? Having mentored developers and switched careers into tech myself, I've put together a roadmap that's helped many navigate their Python journey. Here's a breakdown of key areas to focus on as you level up your Python skills: 1. Core Python    Start with the basics - syntax, variables, and data types. Then move on to control structures and functions. This foundation is crucial. 2. Advanced Python    Once you're comfortable with the basics, dive into decorators, generators, and asynchronous programming. These concepts will set you apart. 3. Data Structures    Get really good with lists, dictionaries, and sets. Then explore more advanced structures. You'll use these constantly. 4. Automation and Scripting    Learn to manipulate files, scrape websites, and automate repetitive tasks. This is where Python really shines in day-to-day work. 5. Testing and Debugging    Writing tests and debugging efficiently will save you countless hours. Start with unittest and get familiar with pdb. 6. Package Management    Understanding pip and virtual environments is crucial for managing projects. Don't skip this. 7. Frameworks and Libraries    Depending on your interests, explore web frameworks like Django, data science libraries like Pandas, or machine learning tools like TensorFlow. 8. Best Practices    Familiarize yourself with PEP standards and stay updated on Python enhancements. Clean, readable code is invaluable. Remember, the key isn't just learning syntax - it's applying what you learn to real projects. Start small, but start building. What area of Python are you currently focusing on?

Are you planning to start your journey to become a proficient Python developer? Here's a roadmap that can help you build a strong foundation and master advanced concepts to excel in the world of Python programming. 🔹 Basics: Start from scratch and learn the fundamental concepts of Python, including basic syntax, variables, data types, and conditional statements. Lay the groundwork for your Python knowledge and coding skills. 🔹 Advanced Topics: Delve into the more intricate aspects of Python, such as iterators, decorators, lambdas, and recursion. Explore how these concepts can enhance your code's efficiency and maintainability. 🔹 Package Managers: Familiarize yourself with essential package managers like PyPI and Pip. Learn how to leverage external libraries and modules to enhance your Python projects and streamline development. 🔹 Testing Your Apps: Discover the importance of testing in Python development. Learn about unittest, pyunit, pytest, doctest, and nose frameworks to ensure your applications are robust and reliable. 🔹 Datastructures and Algorithms: Gain proficiency in essential data structures like arrays, linked lists, sets, dictionaries, and hash tables. Additionally, explore sorting algorithms, binary search trees, heaps, stacks, and queries to optimize data manipulation. 🔹 Version Control Systems: Understand the significance of version control in collaborative coding. Master Git, GitHub, GitLab, and BitBucket to efficiently manage your projects and collaborate with other developers. 🔹 Type Casting and Expectations: Learn how to handle data types effectively and set expectations for your functions. Ensure seamless data manipulation and code reliability through proper type handling. 🔹 Object-Oriented Programming (OOP): Dive into the world of OOP and comprehend classes, methods, inheritance, and dunder methods. Harness the power of OOP to build modular, maintainable, and scalable Python applications. 🔹 Asynchronous Python: Explore the asynchronous world with libraries like gevent, Tornado, aiohttp, and Sanic. Understand the concepts behind asynchronous programming and boost the performance of your web applications. 🔹 Web Frameworks: Learn popular web frameworks like Django, Flask, and Pyramid. Create web applications and RESTful APIs with ease, catering to diverse project needs. What would you like to add? Happy to learn more from the experiences!

Tech writers don’t write. → Not in the way most people think. We don’t sit down with a blank page and “make it up.” We’re not wordsmiths polishing clever sentences. We’re not decorators. We’re architects. And in the age of AI, our role has quietly evolved into something far more powerful—and far more essential. Here’s what the new tech writer actually does: 1.⁠ ⁠We curate. We filter the noise. From dev notes, internal wikis, messy Notion pages, AI-generated drafts—we gather what matters and discard what doesn’t. 2.⁠ ⁠We verify. We don’t just copy and paste. We check, clarify, recheck. Because what’s written in the spec doc isn’t always what’s true in production. 3.⁠ ⁠We restructure. We’re not just editing for grammar. We’re rearchitecting information to match how real users actually read and retain it. Good docs don’t just inform. They guide. 4.⁠ ⁠We translate. We bridge the gap between engineering and end user. Between product complexity and business clarity. Between AI output and human understanding. 5.⁠ ⁠We strategize. We don’t “just write the docs.” We shape documentation ecosystems—mapping user journeys, designing content models, identifying gaps before they become support tickets. If you’re hiring a writer to “clean up” your AI-generated documentation, you’re looking for the wrong skillset. You don’t need a cleaner. You need an operator. One who understands: • How your product works • What your users need • What your GTM team is saying • What your AI tools are missing • And how to bring it all together—seamlessly Because in 2025, tech writers aren’t just writers. We’re content strategists with dev-level instincts. And the companies that understand this? They’re the ones whose products get adopted faster, retained longer, and supported less.

We care a lot about user experience at Duolingo and monitor it via a number of app performance metrics. App performance is especially a challenge on Android because of the breadth of the ecosystem of devices. In 2021, we ran a cross-company Android reboot effort to improve the code architecture and improve latency. We then set latency and performance guardrails to prevent new changes from slowing down the app. Despite our best efforts, though, latency crept up. Early in 2024, one of our data scientists, Daniel Distler, was able to demonstrate that improving latency in some key parts of the user journey would drive solid increases in DAUs (daily active users), one of our main company metrics. This was the nudge we needed to re-invest in the effort. We created a cross-company tiger team to work on improving Android performance. Throughout the year, 20 software engineers participated. In 2024, the team ran 200+ A/B tests on Android performance and delivered remarkable results: - Entry-level device app open conversion jumped from 91% to 94.7% - Entry-level device users experiencing 5+ second app open latency dropped from 39% to just 8% - Hundreds of thousands of DAU gains were directly attributable to these performance enhancements and we expect the actual long-term impact was even larger What work proved most impactful? - Almost half of our DAU impact came from improving code efficiency - Another 20% of impact came from optimizing network requests  - Another chunk came from deferring non-critical work to happen later in key flows - Baseline profiles took a lot of time to get right, but sped up application start-up by 30% Want to learn more? Check out Chenglai Huang and Michael Huang’s blog post: https://lnkd.in/dni58Hez #engineering

Learning Python is an important step to growing your data analyst career. Here is my roadmap to get you started: 1. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝘆𝗻𝘁𝗮𝘅 𝗮𝗻𝗱 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀: Begin by understanding Python’s syntax and getting comfortable with variables, data types, basic operators, and control structures like loops and conditions. This foundation is needed for all the following steps. 2. 𝗖𝗼𝗿𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀: Dive into functions, classes, and modules. These concepts will help you write cleaner, more efficient, and reusable code, even in large applications. 3. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗣𝗮𝗻𝗱𝗮𝘀: Learn to use pandas for data cleaning, transformation, and analysis. Mastering Pandas is important for handling and processing tabular data effectively. 4. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: Explore libraries like Matplotlib and Seaborn to visualize data. Strong visualization skills are necessary to uncover insights and present your findings appealing. 5. 𝗡𝘂𝗺𝗲𝗿𝗶𝗰𝗮𝗹 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆: It is the backbone of many data operations. Understanding how to use NumPy arrays for fast numerical analysis will improve the performance of your data processing. 6. 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗣𝗜𝘀 𝗮𝗻𝗱 𝗪𝗲𝗯 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴: Expand the number of data sources available to you by learning to extract data from the web or APIs. These skills are increasingly valuable in the data analyst’s toolkit. 7. 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Combine Python with SQL using Pandas and SQLAlchemy. Knowing how to retrieve and manipulate data from databases is a must-have for every data analyst. 8. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Learn the basics of machine learning and how to implement them using scikit-learn. This will open a path to predictive analytics and more advanced machine learning techniques. 9. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝘄𝗶𝘁𝗵 𝗚𝗶𝘁: Get to know the basics of Git for version control. This skill is important for collaboration and tracking changes in your code, especially when working on larger projects. 10. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Apply your new skills to real-world projects. This not only deepens your understanding but also builds a portfolio that showcases your capabilities to potential employers. Try to work on topics relevant to your target industry. Data analytics is a fast-evolving field, and continuous learning is needed to stay ahead. Adding Python to your skillset will enable you to build more powerful data workflows and grow your career in the age of AI! Is Python already part of your tech stack, or are you planning to add it soon? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #python #pandas #careergrowth

Users don't suck, but the information provided to them can. If your IFU reads like a legal contract, people won’t read it. Why? Because they’re confusing. Too wordy. Too complex. Too scattered. A great IFU should feel like having a clear-headed expert guiding you step by step. The user needs to know what to do, how to do it, and when to do it. Here's 20 recommendations/writing rules to improve your IFU↴ 1. Write procedures in short, identifiable steps, and in the correct order. 2. Before listing steps, tell the reader how many steps are in the procedure. 3. Limit each step to no more than three logically connected actions. 4. Make instructions for each action clear and definite. 5. Tell the user what to expect from an action. 6. Discuss common use errors and provide information to prevent and correct them. 7. Each step should fit on one page. 8. Avoid referring the user to another place in the manual (no cross-referencing). 9. Use as few words as possible to present an idea or describe an action. 10. Use no more than one clause in a sentence. 11. Write in a natural, conversational way. Avoid overly formal language. 12. Express ideas of similar content in similar form. 13. Users should be able to read instructions aloud easily. Avoid unnecessary parentheses. 14. Use the same term consistently for devices and their parts. 15. Use specific terms instead of vague descriptions. 16. Use active verbs rather than passive voice. 17. Use action verbs instead of nouns formed from verbs. 18. Avoid abbreviations or acronyms unless necessary. Define them when first used and stay consistent. 19. Use lay language instead of technical jargon, especially for medical devices intended for laypersons. 20. Define technical terms the first time they appear and keep definitions simple. Prioritize the user while ensuring MDR/IVDR compliance.

A common misconception about building technical skills is that you need to memorize everything you learn. Trust me, you don't ????? With the resources available today, there's little value in the ability to ramble off every argument of an XLOOKUP function or type fluent code from scratch. What *is* valuable is knowing exactly what that function or code is designed to do, and how it can be applied to solve real-world problems. Think of it this way... Have you memorized the recipe for every meal you've ever cooked? Of course not. Instead, you get familiar with some core ingredients, learn how they can be combined to create different flavors or dishes, and consult a recipe for the details when it's time to cook. Same goes for learning technical skills. In our Excel Formulas course, we teach the core building blocks (or "ingredients") of Excel, and walk through examples of how they can be combined and applied to common business cases (or "recipes"). Instead of asking students to memorize and regurgitate syntax, we focus on describing how each function works and when they can be applied on the job. This is how you build a solid technical foundation and develop practical problem-solving skills – not by memorizing thousands of obscure details. So next time you feel stressed about forgetting something you've learned, cut yourself some slack – and start thinking like a chef!