SQL Queries for Data Analysts: SELECT, GROUP BY, JOINs, Subqueries

📊 7 Months into My Data Analyst Journey – 3 Things That Actually Made a Difference It’s been 4 months since I started working as a Data Analyst, and here are 3 lessons that genuinely helped me grow (beyond just learning tools): 1️⃣ Writing SQL is easy. Writing efficient SQL is a different game. I realized that optimizing queries (using joins wisely, avoiding unnecessary subqueries) can save minutes—or even hours—when working with large datasets. 2️⃣ Excel is still underrated. Before jumping into Python or Power BI, I found that Excel can solve 70% of business problems quickly—especially with Pivot Tables, XLOOKUP, and basic automation. 3️⃣ Understanding the “why” matters more than the “how”. Anyone can build a dashboard. But asking: 👉 What decision will this drive? 👉 Who is the stakeholder? 👉 What metric actually matters? …makes all the difference. 💡 My current focus: Improving storytelling in Power BI dashboards and writing cleaner Python scripts for data cleaning. #DataAnalytics #SQL #Python #Excel #PowerBI #LearningJourney #CareerG

"95% of analysts ignore data distribution, compromising insights." I was working with a large dataset and chose to use Excel, which led to performance issues and errors 📊. I had also failed to handle missing values properly and made incorrect data type conversions. This was a hard lesson to learn, but it made me a better Data Analyst. I've since transitioned to using SQL and Python for data cleaning and visualization. My go-to tools now include Power BI and Tableau for data storytelling and business intelligence ✅ SQL improves performance for large datasets. 📌 Pandas handles missing values efficiently always. 🔷 Incorrect conversions cause significant data errors. 🟠 Python validates data for quality assurance. If you're looking to improve your data cleaning skills and avoid common mistakes, let's connect and discuss how I can help you make data-driven decisions #DataCleaning #DataAnalysis #BusinessIntelligence

Hello Connections 👋 In the journey of a Data Analyst, tools like SQL, Power BI, Python, Tableau, and Excel play a crucial role in solving business problems and deriving insights. But beyond analysis, one of the most critical steps is data cleaning and transformation — and that’s where Power Query (Mashup Language) becomes a game changer. 1)It allows us to handle messy, real-world data efficiently 2)Helps standardize inconsistent formats (like phone numbers, emails, etc.) 3)Enables automation of repetitive data cleaning tasks 4)Improves data quality before it reaches dashboards and reports 5) Saves time and ensures reliable decision-making In this post, I’ve shared a simple yet powerful scenario where we clean and validate contact numbers using Mashup Language (M). Key takeaway: Strong data analysis starts with clean, structured, and reliable data — and mastering Power Query is a must-have skill for every data professional. #DataAnalytics #PowerQuery #DataCleaning #BusinessIntelligence #PowerBI #Excel #DataTransformation #AnalyticsJourney

🚀 Everyone wants to become a Data Analyst… but very few follow the right roadmap. I used to think tools are everything — Excel, SQL, Power BI… But now I understand: 💡 It’s not just about tools, it’s about thinking like an analyst. Here’s the real roadmap I’m following: 1️⃣ Understand Business (how data impacts decisions) 2️⃣ Build strong foundation in Excel & SQL 3️⃣ Learn Visualization (Power BI / Tableau) 4️⃣ Develop Statistics & Critical Thinking 5️⃣ Move to Python for advanced analytics 📌 Most important: Practice on real datasets, not just theory. I’m currently on this journey and improving step by step. If you’re also learning Data Analytics, let’s connect and grow together 🤝 #DataAnalytics #DataAnalyst #Excel #SQL #PowerBI #Python #LearningJourney

🚀 My Data Analyst Learning Roadmap I’ve started a structured journey to strengthen my Data Analytics skills step by step. Here’s the roadmap I’m following: 📊 Excel – Data cleaning, pivot tables, charts, dashboards 🗄️ SQL – SELECT statements, joins, GROUP BY, subqueries 📈 Power BI – Data modeling, DAX, dashboard design 🐍 Python – Pandas, data cleaning, visualization 🧩 Projects – Portfolio, dashboards, and case studies ⏳ Estimated timeline: 12–16 weeks (1–2 hours daily) The goal is simple: build strong fundamentals, practice consistently, and create real-world projects. If you're also learning Data Analytics, feel free to connect — I'd love to share resources and learn together! 🤝 #DataAnalytics #DataAnalyst #LearningJourney #SQL #Python #PowerBI #Excel #CareerGrowth

Everyone wants to become a Data Analyst… but most don’t know where to start. The answer is simpler than you think. You don’t need to learn everything at once. Start with the basics. A simple roadmap looks like this: 1️⃣ Learn Excel Understand sorting, filtering, and basic functions. 2️⃣ Learn SQL This helps you extract and work with data from databases. 3️⃣ Learn a visualization tool like Power BI So you can present your insights clearly. 4️⃣ (Optional) Learn Python For deeper analysis and automation. That’s it. You don’t need 10 tools. You don’t need advanced math. You need clarity and consistency. Learn step by step. Practice on real datasets. Build small projects. Because becoming a Data Analyst is not about learning everything. It’s about learning the right things in the right order. If you’re starting today, just take the first step. #DataAnalytics #DataAnalyst #LearnData #SQL #PowerBI

If I had to learn Data Analysis from scratch in 2026 — here's exactly how I'd do it 👇 Most people overcomplicate it. The truth? 4 tools. Clear sequence. Zero confusion. Step 1 — SQL 🗄️ The foundation of every data job. → Joins & Aggregates → Group By & Having Clause → CTE & Subqueries → Window Functions Step 2 — Excel 📊 Still the most used tool in every office. → Formulas & Functions → VLOOKUP & INDEX → Pivot Tables & Slicers Step 3 — BI Tools 📈 Turn raw data into business decisions. → ETL & Data Integration → Reporting & Analysis → Dashboards & Visualization Step 4 — Python 🐍 (Bonus!) Not mandatory — but a huge career booster. → Pandas & Data Cleaning → Merging DataFrames → Data Visualization 💡 Master these 4 in order and you're job-ready. Save this post 📌 and share with someone starting their data journey! #DataAnalytics #LearnDataAnalysis #SQL #Excel #PowerBI #Python #DataScience #BusinessIntelligence #ETL #Pandas #DataVisualization #DataAnalyst #TechSkills #CareerGrowth #DataSkills #AnalyticsRoadmap #SQLForBeginners #ExcelTips #ShankarMaheshwari #LinkedInLearning

🧠 More Than Just Data📊: The Analyst Mindset A successful data analyst is not just about tools and techniques, but a balance of technical expertise and soft skills. ✅Core Skills ➡️Excel ➡️ SQL ➡️ Python ➡️ Statistics ➡️ Power BI / Tableau ✅Soft Skills ➡️Critical Thinking ➡️ Collaboration ➡️Curiosity ➡️Problem Solving ➡️ Storytelling Mastering both sides helps transform raw data into meaningful insights that drive decisions. 🚀 Currently strengthening my skills every day to grow as a Data Analyst. #DataAnalytics #DataAnalyst #LearningJourney #SQL #Python #PowerBI #Tableau

🚀 Excited to share my first Data Analysis Project: Customer Shopping Behavior Analysis 📊 In this project, I worked on analyzing customer purchase data to uncover business insights and improve decision-making. 🔹 Tools & Skills Used: Python (Pandas, NumPy) SQL (MySQL Queries) Power BI Dashboarding Data Cleaning & EDA Report & Presentation Building 🔹 Key Insights Found: ✔️ Revenue trends by gender ✔️ Top-selling & top-rated products ✔️ Subscription customer behavior ✔️ Age-group wise revenue analysis ✔️ Strategic business recommendations This project helped me understand the complete workflow of a data analyst — from raw data to dashboard storytelling. A special thanks to Amlan Mohanty sir for guiding me throughout this project and helping me learn practical industry-level skills. Your support made this journey easier and more meaningful. 🙏 This is just the beginning, and I’m excited to keep learning, building, and improving every day. Github link : https://lnkd.in/g7sXRYun #DataAnalysis #Python #SQL #PowerBI #DataAnalytics #LearningJourney #Projects #Students #LinkedInLearning

I didn’t realize this when I started... But being a Data Analyst can feel a little lonely. You spend hours: Cleaning data no one else sees. fixing issues no one notices validating numbers again and again Just to make sure everything is… perfect Meanwhile, others just see: “Oh nice dashboard 👍” They don’t see: the 50 SQL queries that failed the Excel sheets that didn’t match the silent panic when numbers don’t add up 🙂 There are days when: You doubt your skills You think you’re too slow You wonder if you’re even doing it right But then… One day, your analysis actually helps someone make a decision. And suddenly, all those quiet hours start to make sense. Being a Data Analyst is not always exciting. It’s patience. It’s persistence. It’s invisible effort. And honestly… that’s what makes it powerful. 👉 If you’re in data, you’re probably doing better than you think. #DataAnalyst #Analytics #LearningEveryday #Data #Python #Excel #SQL #PowerBI #DataCleaning #DataVizualisation #DataScience #ML