#Pca In Data Visualization

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#Pca In Data Visualization Reel by @aibutsimple - Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a new coordinate system where the axes (prin
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@aibutsimple
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a new coordinate system where the axes (principal components) capture the most variance (which has the most amount of detail/information). The computation behind PCA involves calculating the covariance matrix of the data, followed by an eigenvalue decomposition. The eigenvalues represent the amount of variance captured by each principal component, while the corresponding eigenvectors define the directions of these components. Sorting the eigenvalues in descending order allows for selecting the most significant components, reducing dimensionality while keeping the most critical information. C: deepia Join our AI community for more posts like this @aibutsimple 🤖 #deeplearning #machinelearning #datascience #python #programming #dataanalytics #coding #datascientist #data #neuralnetworks #computerscience #computervision #ml #robotics
#Pca In Data Visualization Reel by @deeprag.ai - The math behind PCA is pure linear algebra. 📐🧠 Principal Component Analysis works by re-expressing data in a new coordinate system where the axes a
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@deeprag.ai
The math behind PCA is pure linear algebra. 📐🧠 Principal Component Analysis works by re-expressing data in a new coordinate system where the axes are chosen mathematically, not intuitively. First, the data is mean-centered so variance is measured correctly. Next, PCA computes the covariance matrix, which captures how features vary together. From there, PCA performs an eigenvalue decomposition (or Singular Value Decomposition) to find: • Eigenvectors → the principal directions • Eigenvalues → how much variance each direction explains Projecting the data onto the top-k eigenvectors is just a matrix multiplication, producing a lower-dimensional representation that minimizes reconstruction error in the least-squares sense. Nothing heuristic. Nothing learned. Just geometry, projections, and optimal variance preservation. This is why PCA is foundational to machine learning, statistics, and numerical methods. Credit: deepia Follow @deeprag.ai for math-driven explanations behind modern AI. . . . . . . #PCA #LinearAlgebra #Eigenvectors #Eigenvalues #MatrixDecomposition SVD MathBehindAI MachineLearningMath Statistics DataScience DimensionalityReduction MLTheory STEM
#Pca In Data Visualization Reel by @infusewithai - Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance a
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@infusewithai
Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance as possible in a dataset. It transforms the original correlated variables into a new set of uncorrelated variables called principal components. The process begins by centering the data (which is subtracting the mean), then computing the covariance matrix to capture the relationships between variables. Eigenvectors and their corresponding eigenvalues are then calculated from the covariance matrix. The eigenvectors represent the directions/principal components of the highest variance, while the eigenvalues quantify the amount of variance in each direction. By selecting the top “k” eigenvectors with the highest eigenvalues, PCA projects the data into a lower dimensional space, simplifying analysis and visualization while retaining the most important information. C: deepia
#Pca In Data Visualization Reel by @getintoai (verified account) - PCA: Simplifying Complex Data with Smart Compression 🎯 Principal Component Analysis (PCA) is a powerful dimensionality reduction technique that find
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@getintoai
PCA: Simplifying Complex Data with Smart Compression 🎯 Principal Component Analysis (PCA) is a powerful dimensionality reduction technique that finds key patterns in complex datasets. 💡 How does it work? - Identifies principal components—new axes capturing the most important variations 📊 - Reduces high-dimensional data while preserving key information 🔍 - Helps uncover hidden patterns in data 🤖 PCA is widely used in AI, finance, image processing, and more! Comment "AI" to get the free AI newsletter! 🎥: deepia #AI #MachineLearning #DataScience #PCA #DeepLearning
#Pca In Data Visualization Reel by @chartosaur (verified account) - Just because you can make your chart look unique, doesn't mean you should. A good data visualization is about clarity, not just creativity. When every
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@chartosaur
Just because you can make your chart look unique, doesn't mean you should. A good data visualization is about clarity, not just creativity. When every element - color, font, and shape - works against readability, you're not improving the story, you're burying it. Radial bar charts, for example, may look visually compelling, but they aren't always the best tool. While they do okay in displaying cyclical data, such as seasonal trends or annual sales patterns, their complexity can make your point harder to understand in other contexts. #Charts #Presentation #Viz #PPT #Excel #Graph #Consulting #Mckinsey #Bain #BCG #Vizualization #Slides #Chart #Graphs #Deck #GoogleSlides #StackedBarChart #BarChart #Education #Data #Anchoring #Likert #RadialChart
#Pca In Data Visualization Reel by @mathswithmuza - Principal Component Analysis is a dimensionality-reduction technique that transforms a high-dimensional dataset into a smaller set of new variables ca
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@mathswithmuza
Principal Component Analysis is a dimensionality-reduction technique that transforms a high-dimensional dataset into a smaller set of new variables called principal components. These components are constructed to capture as much of the original variation in the data as possible while remaining uncorrelated with one another. PCA works by identifying directions in the data where the spread is largest, meaning those directions explain the most meaningful structure. Instead of trying to interpret dozens of correlated features, PCA allows you to rotate the coordinate system and focus on just a few axes that summarize most of the information. Once the principal components are found, the data can be projected onto them, making it easier to visualize patterns, clusters, and trends that may be hidden in the original space. This is especially useful in fields like image processing, genetics, or marketing analytics where datasets can have hundreds or thousands of variables. PCA also helps reduce noise by filtering out directions with very little variance, which often correspond to measurement error rather than true structure. Overall, PCA simplifies complex datasets without losing their essential relationships, helping analysts uncover clearer insights and build more efficient models. Like this video and follow @mathswithmuza for more! #math #maths #mathematics #learn #learning #study #studying #coding #ai #chatgpt #foryou #fyp #reels #education #stem #physics #statistics #new #animation #manim #school #university #highschool #college
#Pca In Data Visualization Reel by @skill_uplearn - 📊 MATPLOTLIB - Data Visualization Made Easy 🚀 Agar tum Data Analyst ya Python learner ho, toh Matplotlib MUST learn skill hai 🔥 👉 Isse tum bana
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@skill_uplearn
📊 MATPLOTLIB — Data Visualization Made Easy 🚀 Agar tum Data Analyst ya Python learner ho, toh Matplotlib MUST learn skill hai 🔥 👉 Isse tum bana sakte ho: ✔ Line plots (trends) ✔ Bar charts (comparison) ✔ Scatter plots (relationships) ✔ Histograms (distribution) ✔ Pie charts (proportion) 💡 Real truth: 👉 Data tab tak powerful nahi hota jab tak tum use visualize na karo 🎯 Ye skill tumhe help karegi: ✔ Data analysis projects me ✔ Dashboard banane me ✔ Interviews crack karne me ⚠️ Save this post — ye quick revision guide hai 👉 Follow karo daily Python + Data Analyst content ke liye 🚀 💬 Comment “MATPLOTLIB” agar tum practice questions chahte ho 😎 #matplotlib #python #dataanalysis #datavisualization #datascience pythonforanalytics dataanalyst learnpython coding analytics pythonindia 100daysofcode techskills programming dataskills visualization codingreels reelsindia viralreels
#Pca In Data Visualization Reel by @excel_booster - 📊 Data Visualization Cheat Sheet | Complete Guide to Charts & Graphs In this guide, you'll learn about: ✔️ Gantt Chart - Track project timelines and
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@excel_booster
📊 Data Visualization Cheat Sheet | Complete Guide to Charts & Graphs In this guide, you’ll learn about: ✔️ Gantt Chart – Track project timelines and scheduling ✔️ Bar Chart – Compare categories easily ✔️ Highlight Table – Identify highs and lows quickly ✔️ Bubble Chart – Compare multiple variables visually ✔️ Line Chart – Show trends over time ✔️ Tree Map – Display hierarchical data ✔️ Scatter Plot – Find relationships and correlations ✔️ Pie Chart – Show proportions and percentages ✔️ Box & Whisker Plot – Understand distribution and outliers ✔️ Maps – Visualize geographic data ✔️ Waterfall Chart – Track cumulative values ✔️ Bullet Chart – Measure performance vs targets This cheat sheet is perfect for: 🎯 Data Analysts 🎯 Business Analysts 🎯 Excel & Power BI Users 🎯 Tableau Beginners 🎯 Students & Professionals #DataVisualization #DataAnalytics #ChartsAndGraphs #BusinessAnalytics #ExcelTips PowerBI Tableau DataAnalyst DashboardDesign AnalyticsLearning ExcelBooster
#Pca In Data Visualization Reel by @thephdstudent (verified account) - Data visualisation book recommendation for anyone who wants to turn data into interactive stories, not just static charts 📊🌍💻 ✨ Teaches you how to
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@thephdstudent
Data visualisation book recommendation for anyone who wants to turn data into interactive stories, not just static charts 📊🌍💻 ✨ Teaches you how to move from spreadsheets to web-based visualisations ✨ Covers tools like Google Sheets, Datawrapper, Tableau Public, Chart.js & Leaflet ✨ Perfect if you want to communicate data clearly — even without heavy coding ✨Open-source so freely available online 📌 Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code — Jack Dougherty & Ilya Ilyankou 💭 Summary: This book shows you how to clean, analyse, and visualise data using practical tools — starting with spreadsheets and moving into customisable web-based charts and maps. It’s especially useful if you want to share your work online and make your data interactive, not just informative. If you’re learning data science, bioinformatics, or just want to present your work better, this is a great place to start 🤍 📌 Save this for later — I’ll be sharing more recommendations soon. #womeninstem #datavisualization #datascience #bioinformatics #tech
#Pca In Data Visualization Reel by @python_for_bioinformatics - 🚀 Unlocking Hidden Patterns in Gene Expression! 🧬🔬 Ever wondered how scientists visualize complex gene expression data? 🤔 Enter Principal Compone
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@python_for_bioinformatics
🚀 Unlocking Hidden Patterns in Gene Expression! 🧬🔬 Ever wondered how scientists visualize complex gene expression data? 🤔 Enter Principal Component Analysis (PCA) – the ultimate dimensionality reduction tool! 📉 🔥 Before PCA → Data stuck in a 5D space, impossible to plot! 😵 ✨ After PCA → Data transformed into just two principal components, making it easy to visualize with a scatter plot! 🎯 🔎 What does the plot show? Each dot = a sample 🟡 Closer dots = similar gene expression patterns 🧩 Distant dots = major differences ⚡ PCA makes sense of bioinformatics data in a way we can SEE! 👀💡 Follow for more bioinformatics insights! 🚀📊 Comment "Python" if you want to see a step-by-step implementation of PCA in Python? #Bioinformatics #PCA #MachineLearning #Genomics #DataScience #BigData #GeneExpression #Visualization #ScienceReel #Research #SciComm #Biotech #Python #AI
#Pca In Data Visualization Reel by @datapatashala_official - Various data visualization types 📊📉 Visualizations are powerful tools for making sense of data and communicating insights. From classic charts like
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@datapatashala_official
Various data visualization types 📊📉 Visualizations are powerful tools for making sense of data and communicating insights. From classic charts like bar graphs and line plots to more specialized visualizations like treemaps and bubble charts, there are so many ways to bring your data to life. 🍕 Pie Chart 📊 Bar Chart 📈 Line Chart 🔍 Scatter Plot 📊 Histogram 📊 Treemap 📊 Box Plot 📈 Area Chart 🍩 Donut Chart 💫 Bubble Chart 📊 Flow Chart 📅 Gantt Chart Whether you’re a data analyst, designer, or just love exploring information in creative ways, this overview has something for everyone. Dive in to learn more about each visualization and how to use them effectively! Follow @datapatashala_official #datascience #careerchange #data #Datascientist #dataanalytics #sql #insights #data #dataviz #datavisualization #infographic #charts #graphs #analytics #insights
#Pca In Data Visualization Reel by @deeply.ai - Principal Component Analysis (PCA) is a powerful dimensionality reduction method that simplifies complex datasets by finding the most important patter
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@deeply.ai
Principal Component Analysis (PCA) is a powerful dimensionality reduction method that simplifies complex datasets by finding the most important patterns of variation. Imagine taking some data points and stretching them along their most variable directions (the directions that have the most information); these are called the principal components. These components act like new axes that capture the maximum variability in the data, reducing high-dimensional information to its most important parts. PCA can reveal underlying patterns or features within a dataset. C: deepia Unleash the future with AI. Our latest videos explore using machine learning and deep learning to boost your productivity or create mind-blowing AI art. Check them out and see what the future holds 🤖 #ai #chatgpt #aitools #openai #aitips #machinelearning #deeplyai

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