Shilpi Salwan’s Post

This cheat sheet changed how I see Data Analytics 📊 Before, I was learning tools separately… Now I understand how they actually work together 💡 🔹 SQL → Get the data 🗄️ 🔹 Python → Analyze the data 🐍 🔹 Excel → Explore & present 📈 Step by step, things are starting to make sense 🚀 Still learning. Still building. 💬 What are you focusing on right now? #DataAnalytics #SQL #Python #Excel #LearningJourney #DataAnalyst

This cheat sheet changed how I see Data Analytics 📊 Before, I was learning tools separately… Now I understand how they actually work together 💡 🔹 SQL → Get the data 🗄️ 🔹 Python → Analyze the data 🐍 🔹 Excel → Explore & present 📈 Step by step, things are starting to make sense 🚀 Still learning. Still building. 💬 What are you focusing on right now? #DataAnalytics #SQL #Python #Excel #LearningJourney #DataAnalyst

Wednesday Data Tip: One thing I’m learning while working with data: Don’t rush to conclusions. It’s easy to see a number and assume it tells the full story. But good analysis takes a step back: • Check the context • Validate the assumptions • Look for patterns over time The first insight is not always the right one. Still learning. Still building. #DataAnalytics #SQL #Python #DataAnalysis #LearningInPublic

🚀 Project Spotlight: Data Analysis with Python I recently worked on a data analysis project where I explored data using Python libraries. 🧰 Tools I used: ✔ Pandas ✔ NumPy ✔ Matplotlib ✔ Seaborn 📊 Key Highlights: ✅ Cleaned and processed raw data ✅ Performed statistical analysis ✅ Created meaningful visualizations ✅ Identified patterns and trends 💡 This project helped me understand how data can be transformed into insights. 🔗 More projects coming soon on my GitHub! #DataScience #Python #DataAnalysis #Projects #Learning

📊 Taking data analysis a step further. After working on dashboards in Excel, I explored how Python can be used to handle and analyze data more efficiently. Using Pandas, I worked on a dataset to: • Load and inspect the data • Clean and transform relevant information • Perform analysis to identify patterns and trends One thing I found interesting — tasks that require multiple steps in spreadsheets can be handled more efficiently and consistently using Python. This experience helped me better understand how structured data processing improves both accuracy and scalability in analysis. Looking forward to building on this further. 📌 Code for this analysis: https://lnkd.in/eta7iaaF #Python #Pandas #DataAnalysis #Analytics #Learning

Make Python Your Best Friend in Data 📊 I’ve been building my skills step by step — from reading datasets to transforming, analyzing, and visualizing data. And one thing I’ve learned is this: 👉 You don’t need to memorize everything. You need to understand and practice consistently. So this is one of the cheat sheet l use. Here’s something I believe: We grow faster when we learn with others, not alone. 💬 Drop a function you recognize from the cheat sheet 💬 Tell me what it does (in your own words) 💬 Or add one function you think every data analyst should know Let’s learn from each other and build stronger foundations together. Because the goal isn’t just to write code It’s to think with data #Python #DataAnalysis #DataEngineering #LearningInPublic #DataScience #TechJourney #Coding

Week 14(notes) Python Pandas Essentials for Data Analysis ✨ 🐍 Python + Pandas = Powerful Data Analysis some fundamental Pandas operations that every data analyst should know: 📌 1. View First Rows Use head() to display the first 5 rows of a dataset. df.head() 📌 2. View Last Rows Use tail() to display the last 5 rows. df.tail() 📌 3. Statistical Summary Get quick insights like count, mean, std, min, max using: df.describe() 📌 4. Select Single Column df['Name'] 📌 5. Select Multiple Columns df[['Name', 'Age']] 📌 6. Add New Column df['Salary'] = df['Age'] * 1000 📌 7. Basic Filtering Filter rows based on a condition: df[df['Age'] > 25] 💡 Pandas makes data cleaning and analysis fast, simple, and efficient. #Python #Pandas #DataAnalysis #Data #Aspiring #LinkedInLearning #100DaysOfCode #Analytics #CareerTransition #Techdatacommunity #LearningJourney.

SQL → Python → Excel: Side-by-Side Cheatsheet Still switching between Google tabs to remember syntax? Same problem. Different tools. So I put together this quick cheatsheet 👇 It shows how common data tasks look across: SQL Python (Pandas) Excel From filtering data to joins, aggregations, and more — all in one place. 📌 Save this — you’ll need it more than you think. #DataAnalytics #DataScience #SQL #Python #Excel #DataAnalyst #MachineLearning #Pandas #Analytics #LearnDataScience #DataEngineering #TechCareers #BusinessAnalytics #DataVisualization #CareerGrowth

Really Excited to work with cleaning data is one of the most important steps in data analysis. In Pandas, handling missing values becomes much easier with methods like: • dropna() – remove missing values • fillna() – replace missing values • ffill() – forward fill using previous values • bfill() – backward fill using next values • thresh= – keep rows/columns based on minimum non-null values #Python #Pandas #DataCleaning #DataAnalysis #DataScience

Started learning Pandas — and now data actually makes sense After working with NumPy, I realized something: Handling real-world data (like CSV files) still felt a bit messy. That’s where Pandas comes in. It’s a Python library designed to make working with structured data simple and efficient. 📊 What’s happening here: • read_csv() loads data into a table-like structure • head() shows the first few rows • info() gives a summary of the dataset 💡 What I understood today: – Pandas organizes data in a structured format (DataFrame) – It makes reading and exploring data very easy – This is exactly how real datasets are handled in Data Science This feels like a big step from writing basic programs to actually understanding data. Next: Selecting specific columns and filtering data in Pandas #Python #Pandas #DataAnalysis #MachineLearning #LearningInPublic #DataScience Here is the code: