Nur Hidayah Kamaludin

Being a Data Analyst is more than just SQL and Analysis. A big portion of your time is spent on: - Reporting by building adoption dashboards - Aligning and convincing shareholders - Troubleshooting metric drops - Meetings with the team That's why technical skills are equally important as soft skills. And this what separates average and great Data Analysts! ——— ♻️ Repost this if you find it insightful 👋 Follow Amney for more Daily Tips

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I analyzed 100+ job descriptions for Data Analyst roles. Here's what I discovered and what it means if you're preparing for this role. Job descriptions are like puzzles. If you look close enough, they tell you exactly how to prepare. After going through dozens of listings from companies like Amazon, Deloitte, Infosys, Zoho, and early-stage startups... here's what showed up again and again: - What 95% of Job Descriptions Had in Common: 1. SQL is non-negotiable. You can’t avoid it. It’s the foundation of almost every data role. Hiring managers want to see JOINs, CTEs, GROUP BYs, and subqueries — not just basic SELECTs. 2. Excel + BI Tools (Tableau or Power BI) Employers expect you to build dashboards that simplify complexity. They care less about tools, and more about whether you can translate rows into recommendations. 3. Python: Not always required for entry-level jobs, but mentioned in over 70% of listings especially in companies doing automation, scalable analysis, or advanced modeling. - You’ll use libraries like pandas, numpy, matplotlib, and seaborn not complex AI, but data transformation and storytelling. 4. Data Cleaning & Prep is the Real Work 80% of your time will be spent cleaning, wrangling, merging, and validating data ... not running fancy models. Tools are secondary. Mindset is primary. 5. Basic Statistics & Business Thinking They’re not asking for PhDs. They’re asking: - Can you find patterns? - Can you explain trends? - Can you support decisions? 6. Communication is half the job. Most analysts don’t get rejected for lack of skill. They get rejected because they can’t explain what they did — or why it matters. 🧭 So How Do You Prepare Smart (Not Just Hard)? 1. Start with Structured Learning (No More Random Tutorials) Here are 3 of the most employer-aligned, beginner-to-job-ready certifications I recommend (after reviewing hundreds of listings + community feedback): ✔️ IBM Data Analyst Professional Certificate Covers SQL, Excel, Python, data wrangling, visualization... project-based, flexible, and built by real practitioners. <> https://lnkd.in/gcva2A_j ✔️ Google Data Analytics Professional Certificate Highly respected by hiring teams. Focuses on business-centric analysis, Excel, Tableau, and structured thinking. No prior experience needed. <> https://lnkd.in/gQFKGDaX ✔️ Advanced Data Analysis with Excel (PwC) If you want to master Excel for dashboards, automation, and reporting... this is one of the best business-use-case-focused courses out there. <> https://lnkd.in/g4Y5uiM6 Join NextCareerStep: https://lnkd.in/gna2uuht

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Dear data engineers, if you're learning hands-on by working on projects right now, let me remind you: You don’t need Kubernetes or Docker to deploy your first data pipeline. → Spend your time learning how to write clean, maintainable ETL scripts. You don’t need microservices for your weekend project. → Focus on building a single, reliable service that actually works end to end. You don’t need a fancy data lake with 10 different storage zones. → Start with a simple, well-structured Postgres or SQLite database. You don’t need CI/CD pipelines or 90%+ automated test coverage. → Write tests for the core logic. Get your code working first, then iterate. You don’t need the latest distributed ML frameworks. → Use Pandas, SQL, or even Excel to get your core analysis right. You don’t need “big data” tools if your project fits on your laptop. → Get the basics right: data modeling, simple queries, and understanding your business logic. You don’t need a personal AI assistant or chatbots in your project yet. → Build a tool that solves a real problem, even if it’s basic. 99% of the time, your project isn’t Google-scale (yet). You’re not running a billion-dollar system. What you do need is clarity, speed, and feedback. So build the simplest thing that works. Ship it. Then learn and iterate. The only “best practice” at this stage is: Execute and get feedback, don’t waste months chasing shiny complexity.

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✅ Beginner Roadmap to Become a Data Analyst (2025) 📊🚀 Thinking about starting a career in Data Analytics but not sure where to begin? Here's a clear step-by-step roadmap for beginners in 2025: 🔹 Step 1: Understand the Basics – What is data analytics? – Types: Descriptive | Diagnostic | Predictive | Prescriptive 🔹 Step 2: Master Excel & Google Sheets – Sorting, filtering, pivot tables – Data cleaning and basic dashboards 🔹 Step 3: Learn SQL – Extract, join, and filter data from databases – Practice on Mode | W3Schools | LeetCode 🔹 Step 4: Explore Data Visualization Tools – Start with Power BI or Tableau – Create charts, dashboards & data stories 🔹 Step 5: Learn Python for Analysis – Focus on: Pandas, NumPy, Matplotlib, Seaborn – Clean data, analyze CSVs, build visualizations 🔹 Step 6: Work on Real Projects – Use datasets (sales, marketing, e-commerce, etc.) – Build a portfolio on GitHub 🔹 Step 7: Learn Business Fundamentals – KPIs, ROI, customer metrics – Align insights with real-world business goals 🔹 Step 8: Build Your Resume & Apply! – Highlight your tools, projects, and results – Apply for roles like: → Data Analyst → Business Analyst → Reporting Analyst 💡 Remember: Start small. Stay consistent. Build real projects. Your future in data starts today! 🔥 #DataAnalytics #DataAnalyst #SQL #Python #PowerBI #Tableau #Excel #CareerSwitch #AnalyticsCareers #DataScience #BusinessIntelligence #JobSearch #TechJobs #CareerGrowth #DataVisualization #GitHubProjects #PythonForData #AnalyticsRoadmap #DataDriven #FutureOfWork #UpSkill #LearnToCode #WomenInTech #BeginnerFriendly #LinkedInLearning #CareerTips #EntryLevelJobs #LearningPath #Deloitte #DataJobs2025

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Data roles salary breakdown – 5-figure: learn SQL, Python, data modelling, and data visualisation 6-figure: be an amazing communicator, learn how to lead and influence, understand your business really well, be great at identifying business problems and solving them with data 7-figure: start your own company or be a senior data executive in a big company 8-figure: build and sell a unicorn startup 9-figure: do something illegal 🤷‍♂️

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First interview: Failed. Second interview: Hired. The difference? I prepared for these Power BI questions. Most people fail not because they don’t know Power BI but because they can’t explain it in an interview. Here are 10 common Power BI questions (with answers) every analytics candidate should know: 1️⃣ What is Power BI? – Microsoft tool to visualise, analyse, and share data. 2️⃣ Desktop vs Service? – Desktop: build locally; Service: publish, share, collaborate. 3️⃣ DAX functions? – Formulas for calculations. Example: CALCULATE(SUM(Sales[Amount]), YEAR(Sales[Date])=2024). 4️⃣ Power BI Gateway? – Secure bridge between on-premises data & cloud. 5️⃣ Measures vs Calculated Columns? – Measures = calculated at query time; Columns = stored in the model. 6️⃣ Row-Level Security (RLS)? – Restricts data access for specific users. 7️⃣ Import vs DirectQuery? – Import: cached & fast; DirectQuery: live & slower. 8️⃣ Optimising slow reports? – Reduce visuals, optimise DAX, limit data before loading. 9️⃣ Power Query use? – Clean & transform data before analysis. 🔟 Star vs Snowflake schema? – Star = simpler, faster; Snowflake = more joins, normalised. 💡 Pro Tip: In interviews, explain why you used a feature, not just how. 👍 Like if you’re preparing for interviews 🔁 Repost to help someone in your network ✅ Follow Heena Kouser for job-ready insights & free learning tools #powerbi #dataanalytics

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If I were learning SQL in 2025, Here is exactly what I would do (+ resources) 👇 I have worked as a DS in 3 different companies. I have landed DS offers from 10 different companies. The number 1 skill I’ve used on the job & in interviews? It’s SQL. Yes, I’ve used SQL more than Python as a Data Scientist. So here's how to learn SQL from scratch. 𝟭. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Boring…. can’t we jump start into learning SQL? No! SQL = storing + extracting data from relational DB. So it’s really helpful to know relational databases. K͟e͟y͟ ͟c͟o͟n͟c͟e͟p͟t͟s͟ ↳ Rows vs. columns ↳ Tables vs. schemas vs. database ↳ Keys (primary, foreign & unique) ↳ Indexes ↳ Table relationships ↳ Data types: numeric, string, datetime, boolean Learn relational databases here: https://lnkd.in/gyt3q8AC 𝟮. 𝗟𝗲𝗮𝗿𝗻 𝗯𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 We'll start with getting data out of a SINGLE table. F͟o͟u͟n͟d͟a͟t͟i͟o͟n͟s͟ ↳ SELECT ↳ FROM ↳ WHERE ↳ ORDER BY ↳ LIMIT ↳ AS C͟l͟e͟a͟n͟i͟n͟g͟ ͟d͟a͟t͟a͟ ↳ DISTINCT ↳ LIKE ↳ BETWEEN ↳ COALESCE ↳ CASE WHEN B͟a͟s͟i͟c͟ ͟a͟n͟a͟l͟y͟t͟i͟c͟s͟ ↳ GROUP BY ↳ HAVING ↳ COUNT ↳ SUM ↳ AVG ↳ MIN / MAX How to do analyses with SQL: https://lnkd.in/gvZjepWf 𝟯. 𝗟𝗲𝘃𝗲𝗹 𝘂𝗽 𝘆𝗼𝘂𝗿 𝗦𝗤𝗟 𝘀𝗸𝗶𝗹𝗹𝘀 C͟o͟m͟b͟i͟n͟i͟n͟g͟ ͟t͟a͟b͟l͟e͟s͟ ↳ JOINs (INNER, LEFT, RIGHT, FULL) ↳ UNION and UNION ALL ↳ CTEs vs subqueries W͟i͟n͟d͟o͟w͟ ͟f͟u͟n͟c͟t͟i͟o͟n͟s͟ ↳ OVER ↳ PARTITION BY ↳ ORDER BY ↳ ROWS BETWEEN ↳ SUM, AVG, MIN, MAX with windows ↳ RANK, ROW_NUMBER, NTILE, LAG, LEAD Intermediate SQL: https://lnkd.in/gKM9WkyA Advanced SQL: https://lnkd.in/grhDPTdK 𝟰. 𝗟𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 In the real-world we work with a lot of data at once. This is not a nice-to-have; it’s a must-have skill. Q͟u͟e͟r͟y͟ ͟o͟p͟t͟i͟m͟i͟z͟a͟t͟i͟o͟n͟ ͟t͟i͟p͟s͟ ↳ Avoid unnecessary data processing ↳ Reduce dataset size early ↳ Use indexes wisely ↳ Use EXPLAIN Get practice optimizing your queries: www.interviewmaster.ai 𝟱. 𝗔𝗽𝗽𝗹𝘆, 𝗯𝘂𝗶𝗹𝗱, 𝗮𝗻𝗱 𝗶𝘁𝗲𝗿𝗮𝘁𝗲 Build your own projects. But what projects should you build? Here are some ideas: ↳ Analyzing student’s mental health: https://lnkd.in/gZCUPpr5 ↳ What and where are the world’s oldest businesses: https://lnkd.in/gSWSdVt3 ↳ NYC public school test result scores: https://lnkd.in/g-SCsY5M 𝟲. 𝗣𝗿𝗲𝗽 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗹𝗲𝘀 Learn how SQL is used in the real-world: https://lnkd.in/gZt6bp-F And, of course, practice for SQL interviews - LeetCode: https://lnkd.in/gpcyVPh9 - Interview Master: https://lnkd.in/gvs2u8Bm - StrataScratch: https://lnkd.in/g9D9jZ9A ——— Starting from scratch? Learn all your SQL fundamentals in one place: https://lnkd.in/gNXW297S

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