Seaborn Plots Every Data Analyst Must Know in 2026

📅 Day 9/30 — NumPy Indexing & Slicing Continuing my 30-day journey into data science, today I explored how to efficiently access and manipulate data using NumPy arrays. What I worked on today: 🔢 Accessing elements using indexing (including negative indexing) ✂️ Extracting data using array slicing 🔁 Selecting elements using step slicing 🎯 Using index arrays to pick specific elements 🧠 Applying boolean masking to filter data based on conditions It was interesting to see how NumPy provides powerful ways to quickly access, modify, and filter data, which is very useful when working with large datasets. ➡️ Next step: exploring more advanced NumPy operations and applying them to real-world data. #LearningInPublic #Python #DataScience #NumPy #30DaysOfLearning #ProgrammingJourney

🚀 Day 48 of My 90-Day Data Science Challenge Today I worked on Feature Selection Techniques. 📊 Business Question: How can we select the most important features to improve model performance? Feature selection helps remove irrelevant or redundant features and improves efficiency. Using Python & scikit-learn: • Applied SelectKBest • Used Correlation Analysis • Understood Feature Importance • Reduced dimensionality • Improved model performance 📈 Key Understanding: Not all features are useful — selecting the right ones improves accuracy and speed. 💡 Insight: Removing unnecessary features helps reduce overfitting. 🎯 Takeaway: Better features lead to better models. Day 48 complete ✅ Improving data quality 🚀 #DataScience #MachineLearning #FeatureSelection #Python #LearningInPublic #90DaysChallenge

🚀 Simplifying Trees in DSA! 🌳💻 While Arrays and Linked Lists are great linear structures, hierarchical data requires a Non-Linear approach—like Trees! To make revising easier, I created this visual cheat sheet. Just like a real-world tree has a Root and Leaves, a Tree data structure starts at the Root Node and branches out to Intermediate and Leaf Nodes. Here is what I have visually summarized in these notes: ✅ The core difference between Linear and Non-Linear structures ✅ 7 Types of Trees (including BST, Strict, Complete, and Skew Trees) ✅ Array Representation vs. Logical View ✅ Tree Traversal logic (Pre-order, In-order, Post-order) complete with Python code! 🐍 Visualizing the flow from the root down to the leaf nodes is a game-changer for understanding algorithms. Take a look and let me know in the comments—what is your favorite data structure to work with? 👇 #DSA #DataStructures #Algorithms #Python #CodingJourney #TechNotes #SoftwareEngineering #LearnInPublic

🚀 Day 13 of Sharing My Data Science & Machine Learning Journey  Understanding the "Spread" – Measures of Dispersion Yesterday we found the center; today we find out how much the data deviates from it! 📊 In Data Science, knowing the average (Mean) isn't enough. You need to know if your data points are clustered closely together or scattered far apart. This is where Measures of Dispersion come in. #DataScience #MachineLearning #Statistics #MeasureOfDispersion #Python #LearningJourney

Just learned something interesting today 👇 In Data Analytics, cleaning data takes up almost 70–80% of the total work—not analysis. That means the real skill isn’t just knowing tools like Excel or Python… It’s knowing how to handle messy, real-world data. Small lesson, big perspective shift. What’s something surprising you’ve learned recently? #DataAnalytics #LearningInPublic #DataScience #GrowthMindset

Don't flatten what naturally has structure. It's tempting to model everything in a single class. Easy to write, easy to read, at least until your data grows. This is where most codebases start, with just one model. But with model composition, each model has a single responsibility. And Pydantic handles nested validation automatically. Structure your models the way your domain is actually structured. The code gets cleaner, the errors get clearer, and reuse becomes obvious. This and other real-world modelling patterns are covered in Practical Pydantic: 👉 https://lnkd.in/eGiB7ZxU Model your domain. Not just your data. #Python #Pydantic #Data #Models #Patterns

Days 68-69 of the #three90challenge 📊 Today I explored NumPy operations — specifically indexing and slicing arrays. After understanding NumPy basics, this step made it easier to access and manipulate data efficiently. What I practiced today: • Accessing elements using indexing • Extracting subsets of data using slicing • Working with multi-dimensional arrays • Performing operations on selected data Example thinking: Instead of looping through data manually, I can directly select and operate on specific parts of an array. Example: import numpy as np arr = np.array([10, 20, 30, 40, 50]) print(arr[1:4]) # Output: [20 30 40] This makes data manipulation faster and more intuitive. From handling data → to controlling it efficiently 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #NumPy #DataAnalytics #LearningInPublic #Consistency #Upskilling

📊 Data Science Foundations Series – Part 1: NumPy Basics I’ve started strengthening my fundamentals in data science, beginning with NumPy. Here are some key takeaways: ✅ NumPy is faster than Python lists due to contiguous memory storage ✅ Supports vectorized operations (no need for loops) ✅ Efficient for handling large numerical datasets Some concepts I explored: 🔹 Array creation using np.array() and np.arange() 🔹 Reshaping data with .reshape() 🔹 Indexing and slicing (including negative indexing) 🤯 One interesting learning: m1[-5:-1:-1] returns an empty array. Reason: When stepping backwards, the start index must be greater than the stop index. ✔️ Correct approaches: m1[-1:-5:-1] m1[-5::-1] This small detail helped me better understand how slicing actually works under the hood. 📌 Next: Vectorization & Broadcasting #DataScience #Python #NumPy #LearningInPublic #CareerGrowth

📊 Different Types of Plots in Data Science 🔹 Line Plot → Trends over time 🔹 Bar Plot → Compare categories 🔹 Histogram → Distribution of data 🔹 Scatter Plot → Relationship between variables 🔹 Box Plot → Detect outliers 🔹 Heatmap → Correlation between variables 💡 Choosing the right plot is key to understanding data. #DataVisualization #Python #Analytics

𝐂𝐫𝐚𝐜𝐤𝐞𝐝 𝐭𝐡𝐞 𝐂𝐨𝐝𝐞 𝐨𝐧 𝐇𝐨𝐮𝐬𝐞 𝐏𝐫𝐢𝐜𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧! I just wrapped up a deep dive into Predictive Modeling using the classic California Housing Dataset. Beyond just fitting a model, I focused on clean data visualization and resolving distribution skews to ensure high-performance results. 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:  𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: Linear Regression  𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Modernized EDA using Seaborn histplot & probplot  𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤: Python, Scikit-learn, Pandas, NumPy  𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Managed via a clean, professional GitHub workflow. Check out the full implementation and clean repository in first comment below! #MachineLearning #DataScience #AIEngineering #Python #GitHub #LinearRegression #HousePricePrediction