Sanjay Devrari’s Post

Raw data is never analysis-ready. That’s where the real work begins. 🚀 Project update: Completed the full data cleaning pipeline using Excel + Python. 🔍 What was done: • Profiled 3 datasets (Tickets, Agents, Issues) • Identified real-world data problems • Cleaned data using Pandas • Fixed data types, missing values, inconsistencies • Resolved key issues like duplicate IDs and broken relationships 💡 Key learning: Data cleaning is not just a step — it’s the foundation of accurate analysis. 📊 Current state of data: ✔ Structured ✔ Consistent ✔ Ready for analysis ➡️ Next step: SQL (joins + business insights) 🤔 Quick question: What’s more challenging for you — cleaning data or analyzing it? #DataAnalytics #Python #Pandas #SQL #DataCleaning #LearningInPublic

📊 Day 2 of #100DaysOfBusinessAnalytics One of the most important steps in data analysis is Exploratory Data Analysis (EDA). EDA helps analysts understand the dataset before building models or drawing conclusions. Some key things I usually explore during EDA: • Understanding the dataset structure • Checking for missing values • Identifying patterns and trends • Detecting outliers in the data • Using visualizations to understand distributions EDA helps transform raw data into meaningful insights, which supports better business decision-making. Tools I often use for this process include Python (Pandas, Matplotlib) along with Excel and Power BI for visualization. Learning how to explore data effectively is a key step in becoming a better analyst. #100DaysOfBusinessAnalytics #BusinessAnalytics #DataAnalytics #Python #Excel #PowerBI

🧹 Reality check: 80% of data analysis is cleaning data. Not glamorous. Not complicated. But absolutely necessary. My daily data cleaning routine: ✅ Handle missing values (Pandas: df.dropna() or df.fillna()) ✅ Remove duplicates ✅ Fix data types (dates, numbers, strings) ✅ Standardize formats (names, categories) ✅ Validate against business rules The remaining 20%? Analysis and visualization. But that 20% only works if the 80% is done right. How much of your time goes to data cleaning? #DataCleaning #Python #Pandas #DataAnalytics #RealityCheck

📈 Just finished a small data analysis project and here’s what I learned 👇 Goal: Analyze user behavior and identify trends. Tools used: • SQL for data extraction. • Python (Pandas) for analysis. • Visualization for insights. Key takeaway: The biggest challenge wasn’t coding, it was understanding the data and defining the right metrics. What surprised me: Even simple datasets can reveal powerful insights when you ask the right questions. Next step: Working on improving my data storytelling and dashboard skills. If you're also learning data analytics, what are you currently working on? #DataAnalytics #Python #SQL #Projects #Learning

Before any chart, any model, any dashboard — analysts do this one thing. It's called EDA. Before any chart, any model, any dashboard — analysts do this one thing. It's called EDA. Exploratory Data Analysis. And it saved me from publishing embarrassingly wrong insights. Here's what EDA actually is: Step 1: Look at your data shape → How many rows? Columns? Data types? Step 2: Find missing values → Where are the NULLs? How many? Why? Step 3: Check distributions → Is the data skewed? Any outliers breaking your averages? Step 4: Find relationships → Which columns correlate? What patterns show up? I ran EDA on a vehicle dataset using Python (Pandas + Matplotlib). The first thing I found? 312 duplicate rows. If I'd skipped EDA, my "insights" would've been garbage. EDA isn't glamorous. There are no fancy charts. But it's the difference between analysis and guesswork. What's the most surprising thing you've found during EDA? #DataAnalytics #EDA #Python #DataCleaning #DataScience #Pandas #DataAnalyst

🚀 Data Cleaning Project Update Continuing my work on the Dirty Cafe Sales Data project ☕, today I focused on handling data types and missing values. 🧹 What I did: - Converted columns like Quantity, Price Per Unit, Total Spent into numeric format - Converted Transaction Date into datetime format - Handled missing values in categorical columns by filling them with ""UNKNOWN"" - Filled missing values in numerical columns using median 💡 Proper data type conversion and handling missing values are essential steps to ensure accurate analysis and reliable insights. Step by step turning raw data into analysis-ready data 🚀 #DataAnalytics #Python #Pandas #DataCleaning #Projects #LearningJourney

📅 Day 13 of My Data Analytics Journey 🚀 Today I focused on understanding one of the most important concepts in data analysis — Pandas DataFrames. 🔍 What I learned: • Introduction to Pandas DataFrames • Creating DataFrames from data • Understanding rows and columns • Viewing and exploring data 🧠 Concepts covered: • DataFrame structure (rows & columns) • Column selection and basic operations • Viewing data using ".head()" and ".tail()" • Understanding dataset shape and size 💡 Key Learning: DataFrames provide a structured and efficient way to store and analyze data, making it easier to work with real-world datasets. 📈 Building confidence in handling structured data step by step. 🚀 Next step: Applying filtering and analysis on real datasets. #DataAnalytics #Python #Pandas #LearningInPublic #Consistency #CareerGrowth

Excited to share my latest Data Analysis project on Chocolate Sales 🍫📊 I analyzed sales data using Python (Pandas & Plotly) to uncover key business insights, including: 📈 Sales trends over time 🌍 Top contributing countries 🍫 Best-selling products 📦 Revenue per box & sales volume 📅 Seasonal patterns in sales 💡 The project helped me understand how data can reveal customer behavior, product performance, and business seasonality. Notebook link: https://lnkd.in/dkAH7g3a #DataAnalysis #Python #Pandas #Plotly #EDA #BusinessInsights Waled Saied Shaher Saaed Instant Software Solutions

“How do you actually deal with messy data in real projects?” Because the truth is most datasets are far from perfect. In one of my projects, I worked with thousands of records coming from different sources with missing values, inconsistent formats, duplicate entries… the usual chaos. At first, it felt overwhelming. But over time, I started following a simple approach: 1️⃣ Understand the data before touching it Instead of jumping into coding, I explore patterns, gaps, and inconsistencies. 2️⃣ Clean in layers, not all at once Handling missing values, standardizing formats, and removing duplicates step by step makes the process manageable. 3️⃣ Validate everything Even small errors can lead to wrong insights, so I always cross-check key metrics. 4️⃣ Automate what repeats If a task is done more than twice, it’s worth automating (Python/SQL saves a lot of time here). What I’ve learned is this: 👉 Data cleaning isn’t the “boring part” of analysis, it’s where most of the real work happens. A good model or dashboard is only as good as the data behind it. Curious to know what’s the messiest dataset you’ve worked with? #DataAnalytics #Python #SQL #DataCleaning #DataScience #Analytics

🚀 Sales Data Analysis Project Update Continuing my work on the Sales Data Analysis (500K+ Records) project, I explored another important insight from the dataset. 🔍 New Insight: I identified the Top 5 Highest Selling Items based on sales data. 📊 This analysis helps in understanding which products are in the highest demand and can support better business decisions. 💻 Tools Used: - Python (Pandas, Matplotlib) - CSV Dataset 📁 GitHub Project Link: https://lnkd.in/gu49QiDR I am continuously working on this project and adding more insights step by step. Would love to hear your feedback and suggestions! 🙌 #DataAnalytics #Python #Pandas #Matplotlib #SQL #Projects #LearningJourney