Digital Skills Development

By 2030, these 11 abilities will decide who gets hired Most don’t show up on resumes yet. The World Economic Forum just revealed the top skills for 2030 in the Future of Jobs Report 2025. And it’s a wake-up call. Today's celebrated tech skills? AI will do those better by 2026. Those certifications? Outdated in 18 months. But here's the good news: The skills that matter most in 2030? Technology can't replace them. Start mastering these skills to stay relevant and be recognized: 1. AI and Big Data 🤖 ❌ Passively watch AI replace jobs ✅ Make AI your competitive edge → Use AI to automate weekly reports → Build self-updating dashboards and summaries 2. Analytical Thinking 🧠 ❌ Drown in opinions and noise ✅ Let data drive key decisions → Identify root causes before reacting → Monitor metrics that reveal blind spots 3. Resilience, Flexibility and Agility 🐆 ❌ Break down under shifting priorities ✅ Adapt fast and lead through change → Stay steady during messy execution → Pause, breathe, ask: “What’s the next best move now?” 4. Motivation and Self-Awareness 👤 ❌ Burn out chasing urgency ✅ Work in sync with your energy → Track your energy every 3 hours for a week → Schedule focus work when your mind feels sharp 5. Curiosity and Lifelong Learning 🔍 ❌ Stick to your job description ✅ Learn a complementary skill to your role → If you're in marketing, study basic product design → If you're in finance, explore storytelling with data 6. Leadership and Social Influence 🌟 ❌ Rely on your title for respect ✅ Build trust by how you think, speak and act → Explain why you made a tough call, not just what you decided → Share a client insight that helped your team level up 7. Technological Literacy 💻 ❌ Run to the IT helpdesk for every issue ✅ Build and adapt your own stack → Automate one repetitive workflow today using AI → Use familiar tools more efficiently (Excel, Slack) 8. Systems Thinking 🔧 ❌ React to broken processes ✅ Design workflows that scale → Improve one repeated but inefficient process this week → Ask: “Can this run without me?” 9. Empathy and Active Listening 🎧 ❌ Talk to be heard ✅ Listen to support, inspire and lead → Listen without needing to speak more in 1:1s → Decode what’s really being said 10. Creative Thinking 🎨 ❌ Wait for inspiration ✅ Build innovation into routine → Ask: “What’s another way to solve this?” → Try a small change to test a new idea 11. Talent Management 👥 ❌ Try to do it all ✅ Delegate and develop future leaders → List 3 tasks to delegate now → Improve hiring processes to onboard the right talent 💡 It’s not about doing more. It’s about evolving how you think, lead, and grow. Because the future expects you to. Which one are you focusing on this month? -- ♻ Share this with someone you’d want on your 2030 team. ➕ Follow me (Meera Remani) for future-ready leadership strategies. 🔔 My best insights for transforming your leadership career? Join my exclusive email list. Link below.

Monthly Book Review: Two books for building AI agents - from very different angles. 📘 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (overview, good to start with) This one takes a systems-level view - why agentic systems matter and how they’re shaping the future of autonomy and intelligent applications. In short, it focuses more on big-picture and architecture. It’s more like a guide for people thinking about what agentic AI should become. What stood out to me: - It frames agents not as “chatbots with tools” but as infrastructure for enterprise autonomy - Strong focus on ethics, trust, transparency, and explainability. These are treated as design principles, not just add-ons - Talks about personalization, context-awareness, and decision-making as key challenges - Covers multi-agent collaboration and how agents can plan, adapt, and learn from feedback If you care about the long-term view, this one is worth digging into. 📙 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐋𝐋𝐌𝐬, 𝐑𝐀𝐆, 𝐚𝐧𝐝 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐫𝐚𝐩𝐡𝐬 (practical and more advanced) This one is for builders. Especially if you're working on agents that need reasoning, memory, and retrieval grounding. It’s more technical and focused on real implementation. The book walks through how to combine LLMs with: ➤ 𝐑𝐀𝐆 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 for document-grounded reasoning ➤ 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐠𝐫𝐚𝐩𝐡𝐬 for structured, symbolic understanding ➤ 𝐌𝐮𝐥𝐭𝐢-𝐡𝐨𝐩 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠, 𝐭𝐨𝐨𝐥 𝐮𝐬𝐞, 𝐚𝐧𝐝 𝐬𝐜𝐡𝐞𝐦𝐚 𝐚𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 - the essential components for smart workflows What I found helpful: - Very hands-on, with code, architecture diagrams, and real examples - Focused on making agents reliable and grounded in actual knowledge, not just generating text - Covers planning logic, memory structures, and how to build better reasoning flows It’s more advanced than the first book, but still practical and useful for real-world builds. 𝐌𝐲 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧: ➤ Start with the first if you want to understand the broader vision and systems thinking. ➤ Pick up the second if you're focused on reasoning workflows and building better LLM-powered agents. They go well together. Two perspectives that cover both what to build and how to build it. Let me know if you’ve read either. Curious what others are exploring in this space. Links to both books below: ✔️ Building Agentic AI Systems by Anjanava Biswas and Wrick Talukdar  https://packt.link/HiXtG ✔️ Building AI Agents with LLMs, RAG, and Knowledge Graphs by Salvatore Raieli and Gabriele Iuculano https://packt.link/KgUPm __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #agenticai #aiagents #llms #rag #generativeai

Stop collecting certifications like Pokémon. Most of them won't get you hired. The right certification depends on your role, not the trend. Here's a roadmap by role and skill level: 𝟏. 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 → Beginner: Google Data Analytics, IBM Data Analyst, Excel Associate → Intermediate: Power BI PL-300, Tableau Data Analyst, Google BI → Advanced: Microsoft Fabric, AWS Data Analytics Specialty 𝟐. 𝐁𝐈 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 → Beginner: Google BI, Power BI PL-300, Tableau Desktop Specialist → Intermediate: Tableau Data Analyst, Looker Analyst → Advanced: Microsoft Fabric, Tableau Consultant, CBIP 𝟑. 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭 → Beginner: Tableau Desktop Specialist, Power BI PL-300 → Intermediate: Tableau Data Analyst, Looker Visualization → Advanced: Tableau Consultant, CDVP 𝟒. 𝐒𝐐𝐋 / 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 → Beginner: SQL Basics (DataCamp/Coursera), IBM SQL for Data Science → Intermediate: Azure DP-900, PostgreSQL Associate → Advanced: Azure DP-300, Snowflake SnowPro Core 𝟓. 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐓𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧 → Beginner: AWS Cloud Practitioner, Azure AZ-900 → Intermediate: Databricks Data Engineer Associate → Advanced: Azure DP-203, Google Professional Data Engineer 𝐁𝐮𝐭 𝐡𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐧𝐨 𝐨𝐧𝐞 𝐭𝐞𝐥𝐥𝐬 𝐲𝐨𝐮: You have 6 certificates on your LinkedIn. Zero projects in your portfolio. Guess which one hiring managers actually look at? Certifications open conversations. Projects and portfolios open doors. A certificate says you learned something. A project proves you can do something. Don't chase all of them. Pick based on your domain. Your interest. Your career goals. Then stick with it long enough to build real depth. Depth beats breadth. Every time. What certification are you currently working on? ♻️ Repost if someone in your network needs this roadmap

𝗪𝗵𝘆 𝗟𝗶𝗻𝘂𝘅 𝗦𝗸𝗶𝗹𝗹𝘀 𝗔𝗿𝗲 𝗡𝗼𝗻-𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲 𝗶𝗻 𝗜𝗧 𝗧𝗼𝗱𝗮𝘆  Linux is not just an operating system; it’s the backbone of modern IT infrastructure. Whether it’s web servers, cloud platforms, or embedded systems, Linux is everywhere. If you’re serious about building your career, learning the basics of Linux is a must.  Why Linux Matters   1. 𝗧𝗵𝗲 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗼𝗳 𝗜𝗧 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Linux powers most web, cloud platforms, and enterprise servers.   2. 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: Understanding Linux gives you a deep dive into open-source development and collaboration.   3. 𝗦𝗵𝗲𝗹𝗹 𝗠𝗮𝘀𝘁𝗲𝗿𝘆: Linux shell skills are invaluable for automation and streamlining workflows across IT domains.   4. 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗳𝗼𝗿 𝗗𝗲𝘃𝗢𝗽𝘀 𝗮𝗻𝗱 𝗖𝗹𝗼𝘂𝗱: Most DevOps workflows and cloud services rely heavily on Linux. 5. 𝗕𝘂𝗶𝗹𝘁-𝗜𝗻 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: Its robust security makes Linux indispensable for cybersecurity roles.  Building Your Linux Expertise   1. Learn the basics: core commands, file system navigation, and permissions.   2. Practice shell scripting to automate repetitive tasks.   3. Explore popular distributions like Ubuntu, CentOS, and Fedora.   4. Set up virtual environments to get hands-on experience with Linux systems.   5. Dive into Linux security tools like SELinux, iptables, and auditing.   6. Validate your skills with LPIC, Red Hat, or CompTIA Linux+ certifications.   7. Contribute to open-source projects to gain real-world experience and build connections.  Mastery of Linux can lead to DevOps, cloud engineering, cybersecurity, and beyond opportunities. Consistency and hands-on practice are key to becoming proficient. If you’re looking to future-proof your career, Linux is the skill to invest in.  Where are you in your Linux learning journey? Share your thoughts or tips below!  

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

📢📢 Announcing New WHO Guidance: Digital Transformation Handbook for Primary Health Care 📢📢 https://lnkd.in/e7nxyHQj I’m excited to share the launch of World Health Organization's latest technical guidance, a resource developed through years of collaboration between the Department of Digital Health and Innovation (DHI) and the Human Reproduction Program (HRP). This guidance builds on practical experiences and lessons learned from health systems around the globe. This new resource is more than just a document—it’s a comprehensive roadmap designed to assist countries in digitizing health information for reproductive services. Here’s a look at what it offers: Integration of Digital Tools: How can digital solutions be tailored to enhance patient tracking, improve decision support, and ensure seamless information flow for reproductive health services ? Person-Centered Approaches: Emphasizing continuity of care, the framework integrates systems that connect patients with healthcare providers, making the health journey more accessible, personalized, and data-driven. Standards and Interoperability: Built on global digital standards like SMART Guidelines and HL7 FHIR, this guidance ensures that countries can implement scalable, interoperable solutions that support national health goals. Training and Scale-Up: Countries are supported through a stepwise approach, from design and adaptation to training, deployment, and scaling, making digital transformation achievable at any stage of readiness. Thank you to all who contributed their expertise to this impactful project. Together, we’re supporting resilient, inclusive health systems for a healthier future. 🌍💙 #DigitalHealth #ReproductiveHealth #Innovation #WHO #GlobalHealth #HealthEquity Adele Waugaman Tigest Tamrat Pascale Allotey Tedros Adhanom Ghebreyesus Jeremy Farrar Garrett Mehl Natschja (Nat) R. AeHIN Secretariat Karthik Adapa Derrick Muneene

REEL vs REAL : Data Analyst In REELs and online posts, it looks like: ✔️ Learn SQL ✔️ Learn Python ✔️ Master Excel ✔️ Create dashboards in Power BI / Tableau …and you're set to land your first job! But in REAL life: Project requirements change. Tech stacks are different in every company. Suddenly it’s not just about SQL and Python - it’s also Snowflake, Databricks, AWS, Airflow, Git, scripting, and whatever new tool the team uses. Sometimes it’s internal tools nobody outside the company even knows about. And no matter how many courses you finish, real-world problems will always throw something new your way. The expectation isn’t that you know everything from day one. It’s that you stay curious enough to figure things out. Foundations like SQL, Python, Excel, and Power BI are important - they give you the confidence to start. But building a real career in data goes way beyond ticking off a list of skills. It's about how quickly you can adapt when a tool you’ve never heard of becomes critical to your project. It’s about staying calm when you don’t have all the answers, Googling like a pro, asking good questions, and learning from every messy situation. In real-world data teams, things rarely go by the book. New tech keeps coming in, project needs evolve, and every organization has its own way of doing things. The people who thrive aren’t the ones who knew everything beforehand - they’re the ones who learned how to learn, again and again. ♻️ Repost : If you found this helpful, to reach others who might need it. ✳️ Follow Mariya Joseph for more daily content!

🧩 Network Setup: Breaking Down the Core Components & Workflow For anyone setting up or managing on-premise network infrastructure, here's a simplified breakdown of how key components work together to keep everything running smoothly: 🔹 Patch Panel (Top) The central hub where network cables from wall ports or end-user devices terminate. ✅ Keeps cable management clean and organized. 🔗 Connects directly to the Switch using short patch cables (typically blue). 🔹 Switch (Below the Patch Panel) The traffic controller within your local network (LAN). ✅ Forwards data between devices like PCs, printers, servers, and more. 🔗 Connects to the Patch Panel, Router, and Server. 🔹 Router (Below the Switch) Acts as the bridge between your internal network and the internet (WAN). ✅ Handles IP addressing and firewall rules. 🔗 Connects to the Modem/ISP and Switch. 🔹 Server (Bottom) Provides centralized services such as file storage, internal applications, or databases. 🔗 Connects to the Switch for high-speed access across the LAN. 🔄 How Data Flows (Simplified) When a user in Room A opens a website: Their PC sends the request through a wall port → Patch Panel Patch Panel → Switch Switch → Router Router → Internet via Modem/ISP If the user accesses an internal service, the Switch directs traffic straight to the Server. This setup ensures an efficient, scalable, and secure network foundation for any organization.

As an introvert, I used to struggle with interaction. Luckily AI is becoming the Great Equaliser: 1. Deep learning: Master any topic without a classroom → Tool: NotebookLM transforms documents into podcasts, videos, flashcards, and mind maps → Benefit: Learn complex subjects at your own pace, in the format that suits how you think → Learning guide: https://lnkd.in/exymi6vU 2. Data visualisation: Complex ideas made interactive → Tool: Claude's interactive visuals build charts, diagrams, and tools inside the conversation → Benefit: Turn any concept into something you can click, drag, and explore → Learning guide: https://lnkd.in/eCeEaa3e 3. Design creation: Professional visuals without a designer → Tool: Nano Banana 2 via Google AI Studio creates infographics and 4K images from prompts → Benefit: Production-quality visuals with full creative control, no watermarks → Learning guide: https://lnkd.in/eKrAjaW7 4. Task execution: AI that does the work, not just the thinking → Tool: Claude Cowork reads your files, creates documents, and saves directly to your computer → Benefit: Go from idea to finished deliverable without copy-pasting from chat → Learning guide: https://lnkd.in/eHvgJT3T 5. Data analysis: Your spreadsheets, handled → Tool: Agent Mode in Excel builds dashboards, writes formulas, and generates charts autonomously → Benefit: Describe what you need in plain English. Excel does the rest → Learning guide: https://lnkd.in/eUu-VtaC My take? The possibilities of what we can achieve with AI are endless. Whatever you find challenging, whatever skill you lack, there are now ways to work around it. And that is incredible. The only requirement is intrinsic drive. A desire to learn, to get better, to push yourself forward. Most of these tools are free to get started. The barrier to entry is near zero. As long as you channel that inner agency, the only ceiling is your creativity. Someone who struggles with Excel because maths was never their thing can now have an agent walk alongside them step by step. Someone with a vision for what their product should look like but no design training can now build something professional. The gaps that used to hold people back are closing fast. A whole new game has been created where everyone gets to play. So here's my question to you: How has AI helped you overcome a personal challenge? Share your story below. Image credit: Tobi Lutke on X

Wall Street firms are doubling down on digital assets. Last week's Q2 2025 earnings season exposed a clear divide: while some major banks and firms were relatively silent on digital assets, others positioned themselves as crypto pioneers. Recent legislative developments created more regulatory clarity and running room for financial institutions to explore institutionalizing digital assets, and the market leaders have been front running investments and partnerships and are wasting no time staking leadership claims in the space. Which firms are positioning, partnering, and investing to establish a lead? BlackRock has positioned itself as a leader in shaping the future of finance, with increasing involvement in digital assets, tokenization, and managing stablecoin reserves. Beyond the earnings rhetoric, what is BlackRock doing to drive this innovation? BlackRock's business relationships reveal the depth of their digital asset strategy. Their partnerships span cryptocurrency custody (Coinbase, Anchorage Digital), stablecoin backing (Ethena), and blockchain infrastructure (Injective). They've also invested in digital asset trading platforms like Flowdesk and fintech innovators including Upvest, Texas Stock Exchange, and Sokin; creating a comprehensive ecosystem for digital asset integration across trading, custody, and tokenization. Insights on other major players' digital assets strategies from CB Insights' Earnings Analyst agent insights on their Q2 earnings calls: → Citigroup emerged as another aggressive adopter, with CEO Jane Fraser expressing "high confidence and enthusiasm" about Citi Token Services' ability to provide "multi-asset, multi-bank, cross-border, always-on solutions without needing to partner with other banks." → BNY Mellon and State Street focused heavily on stablecoin infrastructure, with BNY serving as "reserve custodian for Société Générale's first USD stablecoin in Europe" and "primary custodian for Ripple's US stablecoin reserves." State Street's CEO highlighted how "tokenization of money market funds enables uses of these assets in a different way than originally anticipated." CB Insights' Earnings Analyst agent help identify these strategic pivots immediately after calls. Want insights analysis on the major tech firms announcing earnings this week? Comment "Mag7" below for free access to CB Insights' Earnings Analyst breakdown of each Mag7 Q2 2025 quarter and where they are headed.