Vishal Singh’s Post

Most people think data analysis starts with tools. It doesn’t. It starts with the right questions. Over time, I’ve realized that effective data analytics is less about Power BI or Python and more about structured thinking. Here’s the approach I follow when working with a dataset: 1️⃣ Define the problem What decision should this data support? 2️⃣ Perform data exploration (EDA) Identify patterns, missing values, and inconsistencies. 3️⃣ Segment the data Breaking data into groups often reveals insights hidden in totals. 4️⃣ Visualize key trends Using tools like Power BI to turn raw data into clear patterns. 5️⃣ Focus on insights The goal is not just data visualization but meaningful, actionable insights. This process helps transform raw data into business intelligence and better decision-making. Curious... what’s the first thing you focus on when analyzing a dataset? #DataAnalytics #PowerBI #DataScience #BusinessIntelligence #DataVisualization #Analytics #LearningInPublic

Understanding the difference between Data Analytics and Business Analytics is key for anyone stepping into the world of insights and decision-making. 🔹 Data Analytics focuses on exploring raw data to uncover patterns, trends, and correlations. It answers “What happened?” and “What does the data reveal?” using tools like Python, R, SQL, and Power BI. 🔹 Business Analytics, on the other hand, turns those insights into strategic actions. It answers “Why did it happen?” and “What should we do next?” using BI tools, Excel, and SQL to drive decisions and improve performance. Together, they form the backbone of modern business intelligence — transforming data into direction. #DataAnalytics #BusinessAnalytics #PowerBI #SQL #Python #RProgramming #BusinessIntelligence #AnalyticsCareer #DecisionMaking #DataDriven #LinkedInLearning #SadewJayuka

📊 Attended a Data Analytics Session Today! Today, I attended an insightful Data Analytics session from 8 PM to 10 PM. 💡 The session covered important tools and skills like: ✔ Microsoft Excel ✔ Power Query ✔ Artificial Intelligence basics ✔ Power BI ✔ SQL ✔ Python This session helped me understand the core skills required to become a Data Analyst 🚀 Excited to continue learning and start building real-world projects in Data Analytics! #DataAnalytics #DataScience #PowerBI #SQL #Python #LearningJourney #AI

I almost didn’t post this. Not because the project isn’t good… But it’s my first data analytics project. No perfection. No fancy tricks. Just real learning. But then I remembered—everyone you admire started somewhere. So here it is. 👇 I took a raw dataset and asked a simple question: What story is this data trying to tell? And that question led me through the entire analytics journey: • Cleaning messy data with Python. • Exploring patterns and hidden trends. • Writing SQL queries to extract real insights. • Building a Power BI dashboard to visualize everything. • Turning numbers into a clear, structured story. What I discovered: Data isn’t just numbers—it’s behavior, decisions, and opportunities hidden in plain sight. This project isn’t just about tools (Python, SQL, Power BI)… It’s about learning how to think like an analyst. And this is just the beginning. Check it out here: [https://lnkd.in/dWYpqdQi] If you're also learning data analytics, I’d love to connect. 🤝 #DataAnalytics #PowerBI #SQL #Python #LearningInPublic #PortfolioProject #SQI

Most people think data analysis = dashboards. Reality is different. Started working on my project: End-to-End Customer Support Analytics & SLA Performance Dashboard …and everything broke. Same ID → different names Missing relationships Text inside numeric columns Nothing was clean. That’s when it becomes clear: 👉 Data cleaning is not 10% of the job — it’s the job. Before any SQL. Before any Power BI dashboard. Before any “insights”. Right now, working on cleaning messy data using Python (Pandas)… and this is where real learning happens. If your data is wrong, your insights will be wrong. Simple. #DataAnalytics #Python #PowerBI #SQL #LearningInPublic #AnalyticsJourney

Data Analytics is not learned randomly — it follows a structured roadmap. • Start with Excel to understand data handling, cleaning, and basic analysis • Move to visualization tools like Power BI or Tableau to present insights clearly • Learn Python (Pandas, NumPy) for deeper data analysis and manipulation • Understand SQL to work with databases and extract meaningful data • Apply your knowledge through EDA on real-world datasets • Build projects to showcase your skills and create a strong portfolio Following a step-by-step roadmap makes learning more practical, focused, and effective. #DataAnalytics #Roadmap #LearnData #Python #SQL #PowerBI #CareerGrowth #TechSkills #EngineeringStudents #Enginow

Turning raw data into decisions isn’t magic — it’s disciplined tooling and clear objectives. In a recent project I combined SQL, Python and Power BI to deliver actionable insights for sales and operations: 🔍 - Extracted and cleaned 10M+ rows with optimized SQL queries to ensure data integrity. - Used Python for feature engineering and anomaly detection (pandas, scikit-learn) to surface hidden trends. - Built interactive Power BI dashboards that translated models into executive-ready KPIs and visuals. Outcome: a 12% improvement in forecast accuracy and a 20% faster month-end decision cycle. Key lesson: start with the question, not the data. Tools matter, but framing the business problem and iterating with stakeholders drives adoption. 🚀 #DataAnalytics #SQL #Python #PowerBI #BusinessIntelligence

"When it comes to analytics, start small but think big. 📈 I often see analysts jump straight into modeling or complex algorithms—but the real magic happens in the exploration and preparation of data. Understanding trends, identifying anomalies, and cleaning data properly can unlock insights that impact business decisions significantly. In my upcoming post, I’ll share a step-by-step approach to exploratory data analysis (EDA) and building dashboards that really work. Do you usually start with EDA or jump into modeling? Would love to hear your approach!" #DataAnalytics #BusinessIntelligence #PowerBI #Tableau #SQL #Python #Insights

📊 Data Cleaning Steps – The Foundation of Reliable Insights Before jumping into analysis or building models, one step makes all the difference: data cleaning. Here’s a simple framework I follow: 🔍 Explore the dataset – understand structure, types, and quality 🧩 Handle missing data – treat gaps thoughtfully, not blindly 🧹 Remove duplicates – ensure accuracy and uniqueness 🛠️ Fix formatting – keep data consistent and standardized 📈 Manage outliers – investigate before deciding to remove ✅ Validate data – double-check reliability and consistency 💡 Clean data isn’t just a technical step—it directly impacts the quality of decisions, insights, and outcomes. Whether you're working on a small project or large dataset, better data = better results. #DataAnalytics #DataCleaning #DataScience #Analytics #LearningJourney #Excel #Python #DataAnalysis

Day 4: Data Visualization — Turning Data into Insights Raw data alone doesn’t tell a story. Visualization is what makes it understandable. Why visualization matters? Humans understand visuals faster than numbers. A simple chart can reveal patterns that raw data cannot. Common types of plots: * Line chart → trends over time * Bar chart → comparison between categories * Histogram → data distribution * Scatter plot → relationships between variables Simple example (Matplotlib): import matplotlib.pyplot as plt data = [10, 20, 30, 40] plt.plot(data) plt.show() With just a few lines of code, you can turn numbers into meaningful insights. Where visualization is used: * Business reports * Data analysis * Machine learning insights * Decision making Key insight: Good analysis is not just about finding insights — it’s about presenting them clearly. #DataScience #DataVisualization #Python #Matplotlib #Analytics