Sarosh Ramzani’s Post

Excel taught me to think with data. SQL taught me to talk to it directly. 🗄️ I just put together a beginner-to-intermediate presentation on SQL for Data Analysts — and honestly, building it made the concepts click even harder. Here's what surprised me most along the way 👇 🔷 SQL has been the #1 analyst skill for 50+ years — and it still is. LinkedIn's own jobs data backs it up. Every role — analyst, data scientist, BI engineer — lists SQL first. Not Python. Not Tableau. SQL. 🔷 Three clauses run the world. SELECT. FROM. WHERE. Master those, and you can answer 80% of business questions without touching anything else. 🔷 JOINs are just VLOOKUP — but actually powerful. INNER JOIN = matched rows only. LEFT JOIN = all rows + matched rows. FULL JOIN = everything, with NULLs where there's no match. Once this clicked, querying across multiple tables stopped feeling scary. 🔷 GROUP BY is where analytics really begins. COUNT. SUM. AVG. MAX. MIN. These five functions power nearly every report, dashboard, and business summary you'll ever build. 🔷 CTEs > Subqueries. Every time. Subqueries work. CTEs are readable, debuggable, and reusable. Write SQL that your future self (and teammates) can actually understand. 🔷 The real test? A case study. I ran 4 actual business queries on a mock e-commerce dataset: → Revenue by city → Top 5 customers by spend → Customers who never ordered (anti-join pattern) → Product categories with avg order value > $200 Seeing SQL answer real business questions — that's when it stops feeling like "code" and starts feeling like a superpower. 📊 The big takeaway: SQL doesn't replace Excel or Python. It goes before them. Get the data right first. Then analyze. Then visualize. Still learning. Still building. Sharing as I go 🚀 #DataAnalytics #SQL #LearningInPublic #DataScience #CareerGrowth #SQLforAnalysts #DataCleaning #BusinessIntelligence #AnalyticsJourney #PostgreSQL

🚀 SQL is not just a skill — it’s the backbone of Data Analytics. Most beginners think SQL is only about writing SELECT queries… but the reality is much bigger. Here’s a simple SQL mindmap I follow to stay sharp 👇 🔹 DQL (Data Query Language) → SELECT, WHERE, GROUP BY, ORDER BY → Used to extract meaningful insights from data 🔹 DML (Data Manipulation Language) → INSERT, UPDATE, DELETE → Helps you modify and manage data efficiently 🔹 DDL (Data Definition Language) → CREATE, ALTER, DROP → Defines the structure of your database 🔹 Key Concepts You Must Master ✔ Joins (INNER, LEFT, RIGHT) – Combine multiple tables ✔ Aggregations – SUM, COUNT, AVG, MAX, MIN ✔ Window Functions – RANK(), ROW_NUMBER(), LEAD(), LAG() ✔ Filtering – WHERE, HAVING, LIKE, IN, EXISTS 💡 Real Insight: If you don’t understand why you’re writing a query, syntax alone won’t help you crack interviews or solve real problems. 📊 In Data Analyst roles, SQL is used to: • Clean messy data • Analyze trends • Build dashboards • Answer business questions 🎯 My Advice: Don’t just memorize queries. Practice with real datasets and focus on problem-solving. If you're learning SQL right now, focus on building strong fundamentals first — everything else becomes easier. 💬 What’s the most challenging SQL concept for you? #SQL #DataAnalytics #DataAnalyst #Learning #CareerGrowth #TechSkills #BigData #Python #Analytics

Most beginners think they need to learn everything to become a Data Analyst. I used to feel the same. 👉 But this post made one thing clear — you don’t need everything, you need the right order. Because in reality: Trying to learn all tools at once → leads to confusion Focusing on core skills → actually builds confidence That’s the difference. Biggest takeaway for me 👇 👉 Start simple, then build depth step by step For example: • SQL helps you extract and understand data • Excel helps you quickly explore and validate • Python helps you scale and automate Everything else comes after this foundation. One more important realization: 👉 Skills are not equal — some give 80% of results In real work: If you’re strong in SQL + Excel + basic Python You can already solve many business problems So now my focus is: • Not chasing every new tool • Building strong fundamentals first • Going deeper instead of wider Because at the end: 👉 It’s not about how many skills you know… it’s about how well you can use a few. 👉 Reposting the original post below — it gives a clear roadmap 👇 #DataAnalytics #DataAnalyst #SQL #Python #Excel #LearningInPublic #CareerGrowth #TechSkills

🧠 Most people use Excel their whole career. Data Analysts who know SQL? They get hired 3x faster. 💾 Here's everything you need to start — from absolute zero. 👇 📌 What is SQL? Structured Query Language — the standard language to store, retrieve, update & delete data in databases. Every. Single. Data. Tool. Uses it. Power BI · Tableau · Python · Excel · all connect to SQL databases. 🔑 DBMS vs RDBMS — Know the difference: DBMS → Manages data. No strict structure. (MS Access, MongoDB) RDBMS → Tables. Relationships. Rules. Reliability. (MySQL, PostgreSQL, Oracle, SQL Server) RDBMS follows ACID properties: ⚡ Atomicity — All or nothing ✅ Consistency — Data stays valid 🔒 Isolation — Transactions don't interfere 💾 Durability — Data survives crashes 📊 In RDBMS, data looks like this: 🗂️ Table: Employees 👤 ID 1 → Alice Johnson | Data Analytics | ₹75,000 👤 ID 2 → Bob Smith | Business Intelligence | ₹68,000 👤 ID 3 → Charlie Brown | Data Engineering | ₹82,000 Simple. Structured. Powerful. 🎯 This is Part 1 of my complete SQL Series. Follow along — by the end, you'll write queries like a pro. 💾 Save this post — refer back anytime. ♻️ Repost to help someone starting their data journey! 👇 Comment "SQL" if you want the full series! (Algorithm boost trick 😉) #SQL #SQLBasics #DataAnalytics #DataAnalyst #LearnSQL #RDBMS #DatabaseManagement #DataEngineering #PowerBI #Tableau #Python #Excel #TechLearning #SQLSeries #DataScience #CareerGrowth #ShankarMaheshwari #UpskillDaily #DataCommunity #1LakhFollowers

After reviewing 100+ job descriptions for analytics roles, I've found that Excel, SQL, Python/R, and statistical knowledge are key. Tools like Power BI and Tableau are essential, along with strong problem-solving and communication skills. Master these to boost your chances of landing your first analytics job. Tool 1: Power BI (Equivalent Topics in Tableau) 👉 Data Modelling Basics (with Best Practices) 👉 Power Query, Power Pivot (Data Cleaning and Modelling) 👉 Filter and Row Context 👉 Basic M-Language and Intermediate DAX Functions 👉 Measures and Calculated Columns 👉 Types of Charts/Visuals (and Their Use Cases) 👉 Advanced Tooltips, Drill Through Feature 👉 Bookmarks, Filters/Slicers (for Creating Buttons/Page Navigation) 👉 Power BI Service Basics (Schedule Refresh, License Types, Workspace Roles, etc.) Tool 2: SQL (with Any One RDBMS Tool) 👉 SQL Server/MySQL/PostgreSQL (Choose Any One RDBMS) 👉 Database Fundamentals (Primary Key, Foreign Key, Relationships, Cardinality, etc.) 👉 DDL, DML Statements (Commonly Used Ones) 👉 Joins and Unions (Multiple Table Queries) 👉 Views and Stored Procedures 👉 Window Functions (Rank, DenseRank, RowNumber, Lead, Lag) 👉 Basic Select Queries (Single Table Queries) 👉 Subqueries and CTEs Tool 3: MS-Excel (Google Sheets Knowledge is a Plus) 👉 Pivot Tables, Pivot Charts 👉 Various Charts and Their Formatting 👉 Lookups (VLOOKUP, XLOOKUP, HLOOKUP and Their Use Cases) 👉 Conditional Formatting 👉 Major Excel Functions/Formulas (Text, Numeric, Logical Functions) 👉 Basic VBA/Macro 👉 Power Query, Power Pivot Tool 4: Python (Equivalent Topics in R) 👉 Pandas 👉 Matplotlib 👉 Python Libraries/IDEs (e.g., Jupyter Notebook) 👉 Numpy 👉 Python Basic Syntax 👉 Scikit-learn Suggested Learning Combinations for Entry-Level Roles: ➡ Excel + SQL + Power BI (or Tableau) ➡ Excel + SQL + Python (or R) Tip: Master any 3 of these tools to secure an entry-level role, and then upskill on the 4th one after landing the job. Krish Naik Dhaval Patel Sanjay Chandra #dataanalytics #powerbi #sql #python #excel

Why do companies still rely so heavily on SQL in 2026? 🤔 As a Data Analyst, I’ve realized one simple truth — SQL is not just a skill, it’s the foundation of data work. Here’s why companies prefer SQL: 🔹 Direct access to data – No layers, no delays. You can query exactly what you need from the source. 🔹 Efficiency at scale – Handling millions of rows? SQL does it fast and reliably. 🔹 Universal language – Whether it’s MySQL, PostgreSQL, or SQL Server, the core logic remains the same. 🔹 Decision-making speed – Business questions can be answered in minutes, not hours. 🔹 Integration friendly – SQL works smoothly with tools like Power BI, Python, and Excel. In real-world projects, I’ve seen that strong SQL skills often make the difference between just analyzing data and actually solving business problems. If you’re starting your data journey, don’t underestimate SQL — it’s the closest thing we have to a “superpower” in analytics. 💡 #DataAnalytics #SQL #DataAnalyst #Learning #CareerGrowth

𝗬𝗼𝘂𝗿 𝗖𝗩 𝘄𝗼𝗻'𝘁 𝗴𝗲𝘁 𝘆𝗼𝘂 𝗵𝗶𝗿𝗲𝗱 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀. 𝗬𝗼𝘂𝗿 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝘄𝗶𝗹𝗹. Here's how to build one that stands out 👇 🔷 𝗪𝗛𝗔𝗧 is a Data Analytics Portfolio? A portfolio is proof that you can do the work — not just talk about it. It includes: → Real or public datasets you've analysed → SQL queries and Python notebooks → Dashboards built in Power BI or Tableau → A written summary of your findings It's your data resume that speaks louder than words. 🔷 𝗪𝗛𝗬 do most beginners skip building one? Because they think they're not ready yet. The truth? ✅ Recruiters want to see thinking, not perfection ✅ 2 solid projects beat 10 certificates every time ✅ A GitHub + Tableau Public profile = credibility ✅ It forces you to actually apply what you've learned Don't wait to feel ready. Build while learning. 🔷 𝗛𝗢𝗪 to build your first portfolio in 30 days? 𝗪𝗲𝗲𝗸 𝟭 — Pick a dataset (Kaggle, government open data) 𝗪𝗲𝗲𝗸 𝟮 — Clean, explore, and analyse with SQL or Python 𝗪𝗲𝗲𝗸 𝟯 — Build a dashboard in Power BI or Tableau 𝗪𝗲𝗲𝗸 𝟰 — Write up your findings. Upload to GitHub. Share. Repeat with project 2. Then apply. Your first project doesn't need to be perfect. It just needs to exist. ♻️ Repost for every aspiring analyst who needs to hear this. #DataAnalytics #DataPortfolio #DataAnalyst #Kaggle #PowerBI #Tableau #CareerGrowth #SQL #Python #TechSkillAcademy

I have a CS degree. And somehow I still felt completely lost when I first stepped into the world of data analytics. Nobody tells you that knowing how to code doesn't automatically mean you know how to work with data. Those are two different skill sets and building the second one meant starting over in some ways. Here's the tool stack I've built since then, and the real reason behind each choice: 🗄️ SQL This was the first thing I took seriously. Every job posting I looked at had it. Every analyst I followed talked about it. So I stopped putting it off. If you're switching into data, SQL is the one skill with the highest return on your time full stop. 📊 Tableau I tried a few BI tools before settling here. Tableau just clicked for me. The interface is intuitive in a way that let me focus on the story in the data rather than fighting the software. For someone still building confidence, that matters more than you'd think. 📈 Power BI Honestly? I resisted this one for a while. But I kept seeing it in job descriptions and I couldn't ignore it anymore. I just started learning it and the learning curve is humbling but it's everywhere in corporate environments, so here we are. 🐍 Python This one comes from my degree, so it's always felt like home. I use it daily for data work but lately I've been going deeper into FastAPI too. Because at some point I realized: knowing how to analyze data is great, but knowing how to build things with it is a different level entirely. I'm not where I want to be yet. But I'm building every day one tool, one project, one concept at a time. If you're somewhere in the middle of your own transition into data what does your stack look like right now? 👇

🚀 Mastering SQL – The Backbone of Data Analytics💥 In the world of data, Structured Query Language (SQL) is not just a skill — it’s a necessity. Whether you're working in Data Analytics, Data Science, or Backend Development, a strong foundation in SQL can truly set you apart. Here’s a quick snapshot of what a complete SQL toolkit looks like: 🔹 Data Filtering – SELECT, WHERE, DISTINCT 🔹 Sorting & Limiting – ORDER BY, LIMIT, OFFSET 🔹 Aggregations – COUNT, SUM, AVG, GROUP BY, HAVING 🔹 Joins – INNER, LEFT, RIGHT, FULL, CROSS 🔹 Subqueries – Inline, Correlated, EXISTS 🔹 Data Modification – INSERT, UPDATE, DELETE 🔹 Functions – String, Date/Time, Conversion, Conditional 🔹 Window Functions – ROW_NUMBER, RANK, DENSE_RANK 🔹 Indexing – Optimizing performance 💡 Clean queries = Better insights 💡 Efficient queries = Faster performance 💡 Strong SQL = Strong data career As I continue my journey in data analytics, I’m focusing on strengthening my SQL concepts and applying them to real-world datasets. This cheat sheet is a great reminder of how vast and powerful SQL truly is. 📌 Consistency is key — practice daily, build projects, and keep learning. What’s your favorite SQL function or concept? Let’s discuss in the comments 👇 #SQL #DataAnalytics #DataScience #Learning #TechSkills #Database #CareerGrowth #Python #AnalyticsJourney

Excel vs SQL — What Should You Learn First? 📊💻 If you're starting your journey in data analytics, this is one of the biggest questions: Excel or SQL? Let’s make it simple 👇 🔹 Excel 📊 - Beginner-friendly - Great for data cleaning & basic analysis - Perfect for small to medium datasets - Widely used in almost every company 🔹 SQL 💻 - Used to query large databases - Essential for handling big data - More technical but highly powerful - Must-have skill for data roles 🎯 So, what should you learn first? 👉 Start with Excel if you: - Are a complete beginner - Want to understand data basics - Need quick results 👉 Move to SQL if you: - Want to work with large datasets - Aim for data analyst roles - Want to level up your career 💡 The Smart Path: Excel → SQL → (Power BI / Python) 🚀 Master the basics first, then scale your skills. 💬 What are you learning right now — Excel or SQL? #Excel #SQL #DataAnalytics #CareerGrowth #TechSkills #LearningPath #LinkedInGrowth