mailitics

Category: data-visualization

  • Graph Coloring You Can See

    Graph Coloring You Can See Visual intuition with Python The post Graph Coloring You Can See appeared first on Towards Data Science. Rhyd Lewis Go to original source

  • How to Model The Expected Value of Marketing Campaigns

    How to Model The Expected Value of Marketing Campaigns The approach that takes companies to the next level of data maturity The post How to Model The Expected Value of Marketing Campaigns appeared first on Towards Data Science. Rodrigo Almeida Go to original source

  • Multi-Attribute Decision Matrices, Done Right

    Multi-Attribute Decision Matrices, Done Right How to structure decisions, identify efficient options, and avoid misleading value metrics The post Multi-Attribute Decision Matrices, Done Right appeared first on Towards Data Science. Josiah DeValois Go to original source

  • Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning

    Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning Estimating neighborhood-level pedestrian risk from real-world incident data The post Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning appeared first on Towards Data Science. Aneesh Patil Go to original source

  • A Case for the T-statistic

    A Case for the T-statistic And how it compares to the run-of-the-mill z-score The post A Case for the T-statistic appeared first on Towards Data Science. Aniruddha Karajgi Go to original source

  • The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon

    The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon Designing a centralized system to track daily habits and long-term goals The post The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon appeared first on Towards Data Science. Sabrine Bendimerad Go to…

  • Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI

    Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessons learned along the way. The post Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI appeared first on Towards Data Science. Ibrahim Salami Go to original source

  • The Real Challenge in Data Storytelling: Getting Buy-In for Simplicity

    The Real Challenge in Data Storytelling: Getting Buy-In for Simplicity What happens when your clear dashboard meets stakeholders who want everything on one screen The post The Real Challenge in Data Storytelling: Getting Buy-In for Simplicity appeared first on Towards Data Science. Benjamin Nweke Go to original source

  • 4 Ways to Supercharge Your Data Science Workflow with Google AI Studio

    4 Ways to Supercharge Your Data Science Workflow with Google AI Studio With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicate clearer, and automate quicker. The post 4 Ways to Supercharge Your Data Science Workflow with Google AI Studio appeared first on Towards Data Science. Shuai Guo Go to…

  • Geospatial exploratory data analysis with GeoPandas and DuckDB

    Geospatial exploratory data analysis with GeoPandas and DuckDB In this article, I’ll show you how to use two popular Python libraries to carry out some geospatial analysis of traffic accident data within the UK. I was a relatively early adopter of DuckDB, the fast OLAP database, after it became available, but only recently realised that, through…

  • The Machine Learning “Advent Calendar” Day 5: GMM in Excel

    The Machine Learning “Advent Calendar” Day 5: GMM in Excel This article introduces the Gaussian Mixture Model as a natural extension of k-Means, by improving how distance is measured through variances and the Mahalanobis distance. Instead of assigning points to clusters with hard boundaries, GMM uses probabilities learned through the Expectation–Maximization algorithm – the general…

  • The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel

    The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel From local distance to global probability The post The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel appeared first on Towards Data Science. angela shi Go to original source

  • Metric Deception: When Your Best KPIs Hide Your Worst Failures

    Metric Deception: When Your Best KPIs Hide Your Worst Failures The most dangerous KPIs aren’t broken; they’re the ones trusted long after they’ve lost their meaning. The post Metric Deception: When Your Best KPIs Hide Your Worst Failures appeared first on Towards Data Science. Shafeeq Ur Rahaman Go to original source

  • I Cleaned a Messy CSV File Using Pandas .  Here’s the Exact Process I Follow Every Time.

    I Cleaned a Messy CSV File Using Pandas .  Here’s the Exact Process I Follow Every Time. Stop guessing at data cleaning. Use this repeatable 5-step Python workflow to diagnose and fix the most common data flaws. The post I Cleaned a Messy CSV File Using Pandas .  Here’s the Exact Process I Follow Every Time. appeared first on Towards…

  • Natural Language Visualization and the Future of Data Analysis and Presentation

    Natural Language Visualization and the Future of Data Analysis and Presentation Will conversational interaction replace SQL queries, KPI reports, and dashboards? The post Natural Language Visualization and the Future of Data Analysis and Presentation appeared first on Towards Data Science. Michal Szudejko Go to original source

  • Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair)

    Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair) An explanation of time-series visualization, including in-depth code examples in Matplotlib, Plotly, and Altair. The post Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair) appeared first on Towards Data Science. Murtaza Ali Go to original…

  • The Ultimate Guide to Power BI Aggregations

    The Ultimate Guide to Power BI Aggregations Aggregations are one of the most powerful features in Power BI — learn how to leverage this feature to improve the performance of your Power BI solution The post The Ultimate Guide to Power BI Aggregations appeared first on Towards Data Science. Nikola Ilic Go to original source

  • Why Storytelling With Data Matters for Business and Data Analysts

    Why Storytelling With Data Matters for Business and Data Analysts Data is driving the future of business and here’s how you can be prepared for that future The post Why Storytelling With Data Matters for Business and Data Analysts appeared first on Towards Data Science. Rashi Desai Go to original source

  • Does More Data Always Yield Better Performance?

    Does More Data Always Yield Better Performance? Exploring and challenging the conventional wisdom of “more data → better performance” by experimenting with the interactions between sample size, attribute set, and model complexity. The post Does More Data Always Yield Better Performance? appeared first on Towards Data Science. Mohannad Elhamod Go to original source

  • Beyond Numbers: How to Humanize Your Data & Analysis

    Beyond Numbers: How to Humanize Your Data & Analysis The scintillating grid optical illusion is a perfect metaphor for how raw data can mislead us, causing us to see false trends. To escape the “data-rich, action-poor” paradox, organizations should need data humanization. This approach focuses on turning abstract metrics (the what) into clear, actionable stories…

  • What Building My First Dashboard Taught Me About Data Storytelling

    What Building My First Dashboard Taught Me About Data Storytelling Why clarity beats complexity when turning data into stories people actually understand The post What Building My First Dashboard Taught Me About Data Storytelling appeared first on Towards Data Science. Benjamin Nweke Go to original source

  • Using NumPy to Analyze My Daily Habits (Sleep, Screen Time & Mood)

    Using NumPy to Analyze My Daily Habits (Sleep, Screen Time & Mood) Can I use NumPy to figure out how my habits affect my mood and productivity? The post Using NumPy to Analyze My Daily Habits (Sleep, Screen Time & Mood) appeared first on Towards Data Science. Ibrahim Salami Go to original source

  • The Power of Framework Dimensions: What Data Scientists Should Know

    The Power of Framework Dimensions: What Data Scientists Should Know Practical guidance and a case study The post The Power of Framework Dimensions: What Data Scientists Should Know appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Data Visualization Explained (Part 4): A Review of Python Essentials

    Data Visualization Explained (Part 4): A Review of Python Essentials Learn the foundations of Python to take your data visualization game to the next level. The post Data Visualization Explained (Part 4): A Review of Python Essentials appeared first on Towards Data Science. Murtaza Ali Go to original source

  • Hidden Gems in NumPy: 7 Functions Every Data Scientist Should Know

    Hidden Gems in NumPy: 7 Functions Every Data Scientist Should Know I’ve been learning data analytics for a year now. So far, I can consider myself confident in SQL and Power BI. The transition to Python has been quite exciting. I’ve been exposed to some neat and smarter approaches to data analysis. After brushing up…

  • Building A Successful Relationship With Stakeholders

    Building A Successful Relationship With Stakeholders Show your value by moving beyond the technical The post Building A Successful Relationship With Stakeholders appeared first on Towards Data Science. Kristopher McGlinchey Go to original source

  • Data Visualization Explained (Part 3): The Role of Color

    Data Visualization Explained (Part 3): The Role of Color A simple and powerful guide to using color for more impactful data stories. The post Data Visualization Explained (Part 3): The Role of Color appeared first on Towards Data Science. Murtaza Ali Go to original source

  • Plotly Dash — A Structured Framework for a Multi-Page Dashboard

    Plotly Dash — A Structured Framework for a Multi-Page Dashboard An easy starting point for larger and more complicated Dash dashboards The post Plotly Dash — A Structured Framework for a Multi-Page Dashboard appeared first on Towards Data Science. Michael Clayton Go to original source

  • Build a Data Dashboard Using HTML, CSS, and JavaScript

    Build a Data Dashboard Using HTML, CSS, and JavaScript A framework-free guide for Python programmers The post Build a Data Dashboard Using HTML, CSS, and JavaScript appeared first on Towards Data Science. Thomas Reid Go to original source

  • Data Visualization Explained (Part 2): An Introduction to Visual Variables

    Data Visualization Explained (Part 2): An Introduction to Visual Variables A non-technical and accessible guide to the underlying concept behind visual design: visual encoding channels The post Data Visualization Explained (Part 2): An Introduction to Visual Variables appeared first on Towards Data Science. Murtaza Ali Go to original source

  • Decoding Nonlinear Signals In Large Observational Datasets

    Decoding Nonlinear Signals In Large Observational Datasets Rain, snow, or something In between? The post Decoding Nonlinear Signals In Large Observational Datasets appeared first on Towards Data Science. Fraser King Go to original source

  • Data Visualization Explained: What It Is and Why It Matters

    Data Visualization Explained: What It Is and Why It Matters A brief introduction to data visualization and its importance in today’s technological landscape. The post Data Visualization Explained: What It Is and Why It Matters appeared first on Towards Data Science. Murtaza Ali Go to original source

  • A Visual Guide to Tuning Gradient Boosted Trees

    A Visual Guide to Tuning Gradient Boosted Trees Introduction My previous posts looked at the bog-standard decision tree and the wonder of a random forest. Now, to complete the triplet, I’ll visually explore gradient boosted trees! There are a bunch of gradient boosted tree libraries, including XGBoost, CatBoost, and LightGBM. However, for this I’m going…

  • The Crucial Role of Color Theory in Data Analysis and Visualization

    The Crucial Role of Color Theory in Data Analysis and Visualization How research-backed color principles improved clarity and storytelling in my dashboards The post The Crucial Role of Color Theory in Data Analysis and Visualization appeared first on Towards Data Science. Benjamin Nweke Go to original source

  • A Visual Guide to Tuning Random Forest Hyperparameters

    A Visual Guide to Tuning Random Forest Hyperparameters How hyperparameter tuning visually changes random forests The post A Visual Guide to Tuning Random Forest Hyperparameters appeared first on Towards Data Science. James Gibbins Go to original source

  • Hands On Time Series Modeling of Rare Events, with Python

    Hands On Time Series Modeling of Rare Events, with Python This is how to model rare events occurrences in a time series in a few lines of code The post Hands On Time Series Modeling of Rare Events, with Python appeared first on Towards Data Science. Piero Paialunga Go to original source

  • A Visual Guide to Tuning Decision-Tree Hyperparameters

    A Visual Guide to Tuning Decision-Tree Hyperparameters How hyperparameter tuning visually changes decision trees The post A Visual Guide to Tuning Decision-Tree Hyperparameters appeared first on Towards Data Science. James Gibbins Go to original source

  • Where Hurricanes Hit Hardest: A County-Level Analysis with Python

    Where Hurricanes Hit Hardest: A County-Level Analysis with Python Use Python, GeoPandas, Tropycal, and Plotly Express to map the number of hurricane encounters per county over the past 50 years. The post Where Hurricanes Hit Hardest: A County-Level Analysis with Python appeared first on Towards Data Science. Lee Vaughan Go to original source

  • Building a Modern Dashboard with Python and Tkinter

    Building a Modern Dashboard with Python and Tkinter Create polished GUIs and data dashboards with this versatile library The post Building a Modern Dashboard with Python and Tkinter appeared first on Towards Data Science. Thomas Reid Go to original source

  • Mastering NLP with spaCy – Part 3

    Mastering NLP with spaCy – Part 3 Rule-based matching for information extraction The post Mastering NLP with spaCy – Part 3 appeared first on Towards Data Science. Marcello Politi Go to original source

  • “I think of analysts as data wizards who help their product teams solve problems”

    “I think of analysts as data wizards who help their product teams solve problems” Mariya Mansurova explains how hands-on learning, agentic AI, and engineering habits shape her writing and work. The post “I think of analysts as data wizards who help their product teams solve problems” appeared first on Towards Data Science. TDS Editors Go…

  • Confusion Matrix Made Simple: Accuracy, Precision, Recall & F1-Score

    Confusion Matrix Made Simple: Accuracy, Precision, Recall & F1-Score How to evaluate classification models and understand which metric matters the most. The post Confusion Matrix Made Simple: Accuracy, Precision, Recall & F1-Score appeared first on Towards Data Science. Nikhil Dasari Go to original source

  • What Is Data Literacy in 2025? It’s Not What You Think

    What Is Data Literacy in 2025? It’s Not What You Think In today’s fast-paced, distraction-heavy world, data literacy isn’t just about understanding charts or analyzing numbers—it’s about context, clarity, and human connection. With attention spans shrinking and AI-generated insights flooding our screens, even highly skilled professionals can behave like data novices. The real challenge isn’t…

  • How Not to Mislead with Your Data-Driven Story

    How Not to Mislead with Your Data-Driven Story Data storytelling can enlighten—but it can also deceive. When persuasive narratives meet biased framing, cherry-picked data, or misleading visuals, insights risk becoming illusions. This article explores the hidden biases embedded in data-driven storytelling—from the seduction of beautiful charts to the quiet influence of AI-generated insights—and offers practical…

  • I Analysed 25,000 Hotel Names and Found Four Surprising Truths

    I Analysed 25,000 Hotel Names and Found Four Surprising Truths Why are there so many hotels named after cities they are not in? Follow along for a data analysis on hotel names. The post I Analysed 25,000 Hotel Names and Found Four Surprising Truths appeared first on Towards Data Science. Anna Gordun Peiro Go to…

  • How to Overlay a Heatmap on a Real Map with Python

    How to Overlay a Heatmap on a Real Map with Python Visualizing historical tornado trends The post How to Overlay a Heatmap on a Real Map with Python appeared first on Towards Data Science. Lee Vaughan Go to original source

  • 3 Steps to Context Engineering a Crystal-Clear Project

    3 Steps to Context Engineering a Crystal-Clear Project Learn three easy steps for gaining an intelligent picture for any project by using the skill of context engineering. The post 3 Steps to Context Engineering a Crystal-Clear Project appeared first on Towards Data Science. Kory Becker Go to original source

  • Deploy a Streamlit App to AWS

    Deploy a Streamlit App to AWS Using the Elastic Beanstalk service The post Deploy a Streamlit App to AWS appeared first on Towards Data Science. Thomas Reid Go to original source

  • How to Perform Effective Data Cleaning for Machine Learning

    How to Perform Effective Data Cleaning for Machine Learning Learn how you can improve your machine learning models using effective data cleaning The post How to Perform Effective Data Cleaning for Machine Learning appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Interactive Data Exploration for Computer Vision Projects with Rerun

    Interactive Data Exploration for Computer Vision Projects with Rerun Analyse dynamic signals in a computer vision pipeline in Python using OpenCV and Rerun The post Interactive Data Exploration for Computer Vision Projects with Rerun appeared first on Towards Data Science. Florian Trautweiler Go to original source

  • STOP Building Useless ML Projects – What Actually Works

    STOP Building Useless ML Projects – What Actually Works How to find machine learning projects that will get you hired. The post STOP Building Useless ML Projects – What Actually Works appeared first on Towards Data Science. Egor Howell Go to original source

  • An Introduction to Remote Model Context Protocol Servers

    An Introduction to Remote Model Context Protocol Servers Writing, testing and using them. The post An Introduction to Remote Model Context Protocol Servers appeared first on Towards Data Science. Thomas Reid Go to original source

  • Implementing IBCS rules in Power BI

    Implementing IBCS rules in Power BI Is there a way to use the out-of-the-box features of Power BI to be IBCS compliant? The post Implementing IBCS rules in Power BI appeared first on Towards Data Science. Salvatore Cagliari Go to original source

  • From Pixels to Plots

    From Pixels to Plots How I built an AI-powered prototype to turn images into insights The post From Pixels to Plots appeared first on Towards Data Science. Jens Winkelmann Go to original source

  • Building A Modern Dashboard with Python and Taipy

    Building A Modern Dashboard with Python and Taipy A guide to building a front-end data application. The post Building A Modern Dashboard with Python and Taipy appeared first on Towards Data Science. Thomas Reid Go to original source

  • Understanding Application Performance with Roofline Modeling

    Understanding Application Performance with Roofline Modeling A common challenge with calculating an application’s performance is that the real-world performance and theoretical performance can differ. With an ecosystem of products that is growing with high performance needs such as High Performance Computing (HPC), gaming, or in the current landscape – Large Language Models (LLMs), it is…

  • Animating Linear Transformations with Quiver

    Animating Linear Transformations with Quiver A useful tool in your quiver The post Animating Linear Transformations with Quiver appeared first on Towards Data Science. Artemij Lehmann Go to original source

  • Applications of Density Estimation to Legal Theory

    Applications of Density Estimation to Legal Theory A brief analysis using density estimation to compare the two-verdict and three-verdict systems. The post Applications of Density Estimation to Legal Theory appeared first on Towards Data Science. Jimin Kang Go to original source

  • Exploratory Data Analysis: Gamma Spectroscopy in Python

    Exploratory Data Analysis: Gamma Spectroscopy in Python Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source

  • How I Automated My Machine Learning Workflow with Just 10 Lines of Python

    How I Automated My Machine Learning Workflow with Just 10 Lines of Python Use LazyPredict and PyCaret to skip the grunt work and jump straight to performance. The post How I Automated My Machine Learning Workflow with Just 10 Lines of Python appeared first on Towards Data Science. Himanshu Sharma Go to original source

  • Building a Modern Dashboard with Python and Gradio

    Building a Modern Dashboard with Python and Gradio Data insights made simple The post Building a Modern Dashboard with Python and Gradio appeared first on Towards Data Science. Thomas Reid Go to original source

  • A Practical Introduction to Google Analytics

    A Practical Introduction to Google Analytics Learn the key concepts and reports of Google Analytics while practising with the platform The post A Practical Introduction to Google Analytics appeared first on Towards Data Science. Eugenia Anello Go to original source

  • JAX: Is This Google’s NumPy killer?

    JAX: Is This Google’s NumPy killer? Auto differentiation and JIT compilation make a compelling case. The post JAX: Is This Google’s NumPy killer? appeared first on Towards Data Science. Thomas Reid Go to original source

  • How Microsoft Power BI Elevated My Data Analysis and Visualization Workflow

    How Microsoft Power BI Elevated My Data Analysis and Visualization Workflow Explaining useful features every data analyst needs The post How Microsoft Power BI Elevated My Data Analysis and Visualization Workflow appeared first on Towards Data Science. Benjamin Nweke Go to original source

  • I Teach Data Viz with a Bag of Rocks

    I Teach Data Viz with a Bag of Rocks Last Thursday, my co-instructor and I showed up to the Data Visualization course we teach at the University of Washington with a bag of rocks. The bag consisted of a fairly diverse collection that I myself put together across a set of treks in various regions…

  • Optimizing Multi-Objective Problems with Desirability Functions

    Optimizing Multi-Objective Problems with Desirability Functions When working in Data Science, it is not uncommon to encounter problems with competing objectives. Whether designing products, tuning algorithms or optimizing portfolios, we often need to balance several metrics to get the best possible outcome. Sometimes, maximizing one metrics comes at the expense of another, making it hard…

  • Understanding Random Forest using Python (scikit-learn)

    Understanding Random Forest using Python (scikit-learn) Decision trees are a popular supervised learning algorithm with benefits that include being able to be used for both regression and classification as well as being easy to interpret. However, decision trees aren’t the most performant algorithm and are prone to overfitting due to small variations in the training…

  • The Dangers of Deceptive Data Part 2–Base Proportions and Bad Statistics

    The Dangers of Deceptive Data Part 2–Base Proportions and Bad Statistics This is a follow-up to my earlier article: The Dangers of Deceptive Data–Confusing Charts and Misleading Headlines. My first article focused on how visualizations can be used to mislead, diving into a form of data presentation widely used in public matters. In this article,…

  • Real-Time Interactive Sentiment Analysis in Python

    Real-Time Interactive Sentiment Analysis in Python You know what the best part of being an engineer is? You can just build stuff. It’s like a superpower. One rainy afternoon I had this random idea of creating a sentiment visualization of a text input with a smiley face that changes it’s expression base on how positive…

  • Fine-Tuning vLLMs for Document Understanding

    Fine-Tuning vLLMs for Document Understanding In this article, I discuss how you can fine-tune VLMs (visual large language models, often called vLLMs) like Qwen 2.5 VL 7B. I will introduce you to a dataset of handwritten digits, which the base version of Qwen 2.5 VL struggles with. We will then inspect the dataset, annotate it,…

  • Modern GUI Applications for Computer Vision in Python

    Modern GUI Applications for Computer Vision in Python Introduction I’m a huge fan of interactive visualizations. As a computer vision engineer, I deal almost daily with image processing related tasks and more often than not I am iterating on a problem where I need visual feedback to make decisions. Let’s think of a very simple image…

  • Struggling to Land a Data Role in 2025? These 5 Tips Will Change That

    Struggling to Land a Data Role in 2025? These 5 Tips Will Change That Breaking into the tech world is no longer as easy (or glamorous) as it used to be. Lots of people are finding it difficult to find their way into the current tech market. This can be due to lots of reasons…

  • Plotly’s AI Tools Are Redefining Data Science Workflows 

    Plotly’s AI Tools Are Redefining Data Science Workflows  Is there anything more frustrating than building a powerful data model but then struggling to turn it into a tool stakeholders can use to achieve their desired outcome? Data Science has never been short on potential but is also never short on complexity. You can refine algorithms…

  • Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models

    Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models I used to avoid time series analysis. Every time I took an online course, I’d see a module titled “Time Series Analysis” with subtopics like Fourier Transforms, autocorrelation functions and other intimidating terms. I don’t know why, but I always found a reason to avoid…

  • PyScript vs. JavaScript: A Battle of Web Titans

    PyScript vs. JavaScript: A Battle of Web Titans We’re delving into frontend web development today, and you might be thinking: what does this have to do with Data Science? Why is Towards Data Science publishing a post related to web dev? Well, because data science isn’t only about building powerful models, engaging in advanced analytics,…

  • No More Tableau Downtime: Metadata API for Proactive Data Health

    No More Tableau Downtime: Metadata API for Proactive Data Health In today’s world, the reliability of data solutions is everything. When we build dashboards and reports, one expects that the numbers reflected there are correct and up-to-date. Based on these numbers, insights are drawn and actions are taken. For any unforeseen reason, if the dashboards are…

  • What Germany Currently Is Up To, Debt-Wise

    What Germany Currently Is Up To, Debt-Wise €1,600 per second. That’s how much interest Germany has to pay for its debts. In total, the German state has debts ranging into the trillions — more than a thousand billion Euros. And the government is planning to make even more, up to one trillion additional debt is…

  • The Dangers of Deceptive Data–Confusing Charts and Misleading Headlines

    The Dangers of Deceptive Data–Confusing Charts and Misleading Headlines “You don’t have to be an expert to deceive someone, though you might need some expertise to reliably recognize when you are being deceived.” When my co-instructor and I start our quarterly lesson on deceptive visualizations for the data visualization course we teach at the University…

  • Do European M&Ms Actually Taste Better than American M&Ms?

    Do European M&Ms Actually Taste Better than American M&Ms? (Oh, I am the only one who’s been asking this question…? Hm. Well, if you have a minute, please enjoy this exploratory Data Analysis — featuring experimental design, statistics, and interactive visualization — applied a bit too earnestly to resolve an international debate.) 1. Introduction 1.1…

  • Publish Interactive Data Visualizations for Free with Python and Marimo

    Publish Interactive Data Visualizations for Free with Python and Marimo Working in Data Science, it can be hard to share insights from complex datasets using only static figures. All the facets that describe the shape and meaning of interesting data are not always captured in a handful of pre-generated figures. While we have powerful technologies…

  • 4-Dimensional Data Visualization: Time in Bubble Charts

    4-Dimensional Data Visualization: Time in Bubble Charts Bubble Charts elegantly compress large amounts of information into a single visualization, with bubble size adding a third dimension. However, comparing “before” and “after” states is often crucial. To address this, we propose adding a transition between these states, creating an intuitive user experience. Since we couldn’t find…

  • How to Create Network Graph Visualizations in Microsoft PowerBI

    How to Create Network Graph Visualizations in Microsoft PowerBI Microsoft PowerBI is a one of the most popular Business Intelligence (BI) tools, and while it has all the features you need to create dynamic analytic reporting for stakeholders across the business, creating some advanced data visualizations is more challenging. This article will walk through how…

  • Myths vs. Data: Does an Apple a Day Keep the Doctor Away?

    Myths vs. Data: Does an Apple a Day Keep the Doctor Away? Introduction “Money can’t buy happiness.” “You can’t judge a book by its cover.” “An apple a day keeps the doctor away.” You’ve probably heard these sayings several times, but do they actually hold up when we look at the data? In this article series,…

  • Awesome Plotly with code series (Part 9): To dot, to slope or to stack?

    Awesome Plotly with code series (Part 9): To dot, to slope or to stack? Simple methods to replace cluttered bar charts with crisp, reader-friendly visuals. Continue reading on Towards Data Science » Jose Parreño Go to original source

  • Rapid Data Visualization with Copilot and Plotly

    Rapid Data Visualization with Copilot and Plotly Code visualizations quickly and efficiently with Copilot, Plotly, and Streamlit Continue reading on Towards Data Science » Alan Jones Go to original source

  • Water Cooler Small Talk, Ep 7: Anscombe’s Quartet and the Datasaurus

    Water Cooler Small Talk, Ep 7: Anscombe’s Quartet and the Datasaurus Why descriptive statistics aren’t enough and plotting your data is always essential Continue reading on Towards Data Science » Maria Mouschoutzi, PhD Go to original source

  • Showcasing Soaring Wildfire Counts With Streamlit and Python: A Powerful Approach

    Showcasing Soaring Wildfire Counts With Streamlit and Python: A Powerful Approach Analyzing historical wildfire trends in Canada with public data Continue reading on Towards Data Science » John Loewen, PhD Go to original source

  • Data behind the Luck, Ambition, and a Billion-Dollar Dream: Lottery

    Data behind the Luck, Ambition, and a Billion-Dollar Dream: Lottery Using Seattle’s local retail store data for consumer patterns of the lottery (SQL, Python) Continue reading on Towards Data Science » Sunghyun Ahn Go to original source

  • Awesome Plotly with Code Series (Part 7): Cropping the y-axis in Bar Charts

    Awesome Plotly with Code Series (Part 7): Cropping the y-axis in Bar Charts Is there ever a good reason for starting a bar chart above zero? Continue reading on Towards Data Science » Jose Parreño Go to original source

  • Will Your Christmas Be White? Ask An AI Weather Model!

    Will Your Christmas Be White? Ask An AI Weather Model! Learn how to visualize AI weather and create your own forecast for the holidays Continue reading on Towards Data Science » Caroline Arnold Go to original source

  • 5 Essential Tips to Build Business Dashboards Stakeholders Love

    5 Essential Tips to Build Business Dashboards Stakeholders Love A practical guide to designing clear, effective, and actionable dashboards for decision-making Continue reading on Towards Data Science » Yu Dong Go to original source

  • Awesome Plotly with Code Series (Part 5): The Order in Bar Charts Matters

    Awesome Plotly with Code Series (Part 5): The Order in Bar Charts Matters And it is not always simply ordering by highest to lowest Continue reading on Towards Data Science » Jose Parreño Go to original source

  • Step-by-Step Guide for Building Bump Charts in Plotly

    Step-by-Step Guide for Building Bump Charts in Plotly Learn how to create custom bump charts in Python using Plotly for data visualization Continue reading on Towards Data Science » Amanda Iglesias Moreno Go to original source

  • When Not to Use the Streamlit AgGrid Component

    When Not to Use the Streamlit AgGrid Component Streamlit-AgGrid is amazing. But there are 2 scenarios where its use is not recommended. Continue reading on Towards Data Science » Jose Parreño Go to original source

  • A quick guide to Network Science

    A quick guide to Network Science For those who would like to learn about complex connections — from theory to practice in Python Continue reading on Towards Data Science » Milan Janosov Go to original source

  • Addressing Missing Data

    Addressing Missing Data Understand missing data patterns (MCAR, MNAR, MAR) for better model performance with Missingno Continue reading on Towards Data Science » Gizem Kaya Go to original source