Zachary Pinto

Mistakes aspiring data analysts are making today and what needs to change. The job market for entry level data roles is very competitive but everyone is applying for the same analytics jobs with the exact same playbook. And wondering why they are not getting your first job. The market is competitive but those who take extra and relevant steps are crushing it. These are 4 mistakes you need to avoid if you are an aspiring analyst wanting to get your first job: 📍You're stuck in tutorial hell 5 bootcamps. 12+ certificates. Zero real projects. Moving from one bootcamp to another wouldn't make you better but getting your hands dirty would. Learning ≠ Doing. New rule: For every new skill learned or a course completed, build a project no matter how small it will be. Stop consuming. Start creating. 📍Building the same portfolios as every one else. Scroll through analyst portfolios on LinkedIn or GitHub. Titanic survival rates. COVID dashboards. Sales data from Kaggle. Use the guided projects on YouTube and other platform for learning and not for your portfolio. Do not focus on just replicating the dashboards but focus on understanding every approach and the reasons behind them. Build your own thing with the skills you will acquire. In real world, no one will create for you to replicate. 📍We're all making pretty dashboards that say nothing Everyone's copying those gorgeous Dribbble designs. Beautiful colors. Clean layouts. Zero insight. Beyond the good looking visuals, here's what differentiates you: focus on telling story and providing actionable insights. Study data visualization fundamentals before aesthetics. Understand when to use which chart type and why. Learn that less is often more and every element should serve a purpose. 📍 No niche or industry Most analysts do not know the industry or the space they want to operate in. Analyst are needed in every industry but.. To stand out → Pick ONE industry. Go deep. Learn the metrics, problems, and language. I focused on finance—built an automated earnings report with Netflix's 10-K and other financial reports with Power BI and those opened the doors for me. 👊The competitive advantage nobody talks about: It's not about moving from one bootcamp to another or having a portfolio with all YouTube projects. It's about being completely different in your approach. → build your own projects with your unique skills → Focus on an industry you actually understand or want to play in. → Build projects that solve real problems, not tutorial exercises → build reports and dashboards that solve problems or provide actionable insights. The market doesn't reward you for doing what everyone else does. It rewards you for being different. If you are already in the space, what advise would you want to add? Just share in the comment.

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The reality of being a graduate Data analyst. What They Don’t Tell You About Being a Data Analyst Everyone talks about dashboards, models, and predictions. But in reality? • You spend hours cleaning inconsistent data • You explain why “insight” isn’t the same as “correlation” • You work hard to make complex things simple for non-technical teams • You fix broken queries — and sometimes, broken expectations That’s what I’ve been learning firsthand — from academic projects to building real-world dashboards and running regression models to predict property prices in Pakistan. The more I learn, the more I realise: Technical skills matter, but communication is everything, especially in the UK job market Curious to hear from others in the field: What’s the one “non-technical” skill every data analyst needs to succeed? #DataAnalytics #OpenToWork #DataAnalyst #PowerBI #SQL #Python #MachineLearning #AnalyticsCommunity #CommunicationInTech #NorthumbriaUniversity

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These Data Analyst projects got me my first job after I pivoted from a career in oil/gas engineering to data analytics. I had people reaching out on LinkedIn to ask about them and my interviews were mostly centred around them as I didn’t have prior data experience. Two of them also got me unsolicited referrals. Check the comment section to get the relevant links. Let me know if you find it useful. I also have waiting list for my next Data Analyst job prep cohort if you want to join us, sign up! #dataanalytics #dataanalyst #datascience #dataanalystjobs

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🔍 My biggest takeaway as a Junior Data Scientist intern? Frameworks matter more than shortcuts — here’s how I proved it. 🌐 My journey as a Junior Data Scientist Intern at KNIT Finance During my internship, I focused on applying Data Science to real-world blockchain problems. What mattered most wasn’t just the tools, but the mindset and process I followed: 1️⃣ Start with clarity – Broke down market data on TradingView, framed key questions like “What does a top trader’s behaviour look like?” 2️⃣ Build frameworks, not shortcuts – Integrated APIs from Arkham, CEXs, and DEXs to create predictive/statistical models. 3️⃣ Tackle real constraints – Faced data scarcity, so I designed a workflow: Ideation → Backtesting → Paper Trading → Forward Testing. 4️⃣ Think adaptive – Automated real-time data to Google Sheets, used stats to calculate dynamic liquidity ranges, and scaled across ETH/USDT and BNB/USDT pairs. 5️⃣ Deliver with impact – Developed a draft Liquidity Bot and deployed workflows on AWS, connecting theory with execution. 💡 This experience taught me: Ask the right questions first, Build modular workflows step by step, Balance creativity with practical constraints. 🙏 Grateful to P. Naga Sai Kanishka for constant guidance and Sainath Gupta ₿ Sir for this amazing opportunity. #DataScience #MachineLearning #Blockchain #Internship #ProblemSolving #Crypto #Data

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