Category: data-science
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How I Optimized My Leaf Raking Strategy Using Linear Programming
How I Optimized My Leaf Raking Strategy Using Linear Programming From a weekend chore to a fun application of valuable operations research principles The post How I Optimized My Leaf Raking Strategy Using Linear Programming appeared first on Towards Data Science. Josiah DeValois Go to original source
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2025 Must-Reads: Agents, Python, LLMs, and More
2025 Must-Reads: Agents, Python, LLMs, and More Don’t miss our most popular articles of the past year! The post 2025 Must-Reads: Agents, Python, LLMs, and More appeared first on Towards Data Science. TDS Editors Go to original source
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The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel
The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel Understanding forward propagation and backpropagation through explicit formulas The post The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel appeared first on Towards Data Science. angela shi Go to original source
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A Practical Toolkit for Time Series Anomaly Detection, Using Python
A Practical Toolkit for Time Series Anomaly Detection, Using Python Here’s how to detect point anomalies within each series, and identify anomalous signals across the whole bank The post A Practical Toolkit for Time Series Anomaly Detection, Using Python appeared first on Towards Data Science. Piero Paialunga Go to original source
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The Machine Learning “Advent Calendar” Day 17: Neural Network Regressor in Excel
The Machine Learning “Advent Calendar” Day 17: Neural Network Regressor in Excel Neural networks often feel like black boxes. In this article, we build a neural network regressor from scratch using only Excel formulas. By making every step explicit, from forward propagation to backpropagation, we show how a neural network learns to approximate non-linear functions…
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3 Techniques to Effectively Utilize AI Agents for Coding
3 Techniques to Effectively Utilize AI Agents for Coding Learn how to be an effective engineer with coding agents The post 3 Techniques to Effectively Utilize AI Agents for Coding appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Separate Numbers and Text in One Column Using Power Query
Separate Numbers and Text in One Column Using Power Query An Excel sheet with a column containing numbers and text? What a mess! The post Separate Numbers and Text in One Column Using Power Query appeared first on Towards Data Science. Salvatore Cagliari Go to original source
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The Machine Learning “Advent Calendar” Day 16: Kernel Trick in Excel
The Machine Learning “Advent Calendar” Day 16: Kernel Trick in Excel Kernel SVM often feels abstract, with kernels, dual formulations, and support vectors. In this article, we take a different path. Starting from Kernel Density Estimation, we build Kernel SVM step by step as a sum of local bells, weighted and selected by hinge loss,…
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6 Technical Skills That Make You a Senior Data Scientist
6 Technical Skills That Make You a Senior Data Scientist Beyond writing code, these are the design-level decisions, trade-offs, and habits that quietly separate senior data scientists from everyone else. The post 6 Technical Skills That Make You a Senior Data Scientist appeared first on Towards Data Science. Piero Paialunga Go to original source
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Lessons Learned from Upgrading to LangChain 1.0 in Production
Lessons Learned from Upgrading to LangChain 1.0 in Production What worked, what broke, and why I did it The post Lessons Learned from Upgrading to LangChain 1.0 in Production appeared first on Towards Data Science. Clara Chong Go to original source
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The Machine Learning “Advent Calendar” Day 14: Softmax Regression in Excel
The Machine Learning “Advent Calendar” Day 14: Softmax Regression in Excel Softmax Regression is simply Logistic Regression extended to multiple classes. By computing one linear score per class and normalizing them with Softmax, we obtain multiclass probabilities without changing the core logic. The loss, the gradients, and the optimization remain the same. Only the number…
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The Machine Learning “Advent Calendar” Day 13: LASSO and Ridge Regression in Excel
The Machine Learning “Advent Calendar” Day 13: LASSO and Ridge Regression in Excel Ridge and Lasso regression are often perceived as more complex versions of linear regression. In reality, the prediction model remains exactly the same. What changes is the training objective. By adding a penalty on the coefficients, regularization forces the model to choose…
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NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating
NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating This one little trick can bring about enhanced training stability, the use of larger learning rates and improved scaling properties The post NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating appeared first on Towards Data Science. Sean Moran Go to original…
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The Machine Learning “Advent Calendar” Day 12: Logistic Regression in Excel
The Machine Learning “Advent Calendar” Day 12: Logistic Regression in Excel In this article, we rebuild Logistic Regression step by step directly in Excel. Starting from a binary dataset, we explore why linear regression struggles as a classifier, how the logistic function fixes these issues, and how log-loss naturally appears from the likelihood. With a…
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EDA in Public (Part 1): Cleaning and Exploring Sales Data with Pandas
EDA in Public (Part 1): Cleaning and Exploring Sales Data with Pandas Hey everyone! Welcome to the start of a major data journey that I’m calling “EDA in Public.” For those who know me, I believe the best way to learn anything is to tackle a real-world problem and share the entire messy process — including mistakes, victories,…
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The Machine Learning “Advent Calendar” Day 11: Linear Regression in Excel
The Machine Learning “Advent Calendar” Day 11: Linear Regression in Excel Linear Regression looks simple, but it introduces the core ideas of modern machine learning: loss functions, optimization, gradients, scaling, and interpretation. In this article, we rebuild Linear Regression in Excel, compare the closed-form solution with Gradient Descent, and see how the coefficients evolve step…
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7 Pandas Performance Tricks Every Data Scientist Should Know
7 Pandas Performance Tricks Every Data Scientist Should Know What I’ve learned about making Pandas faster after too many slow notebooks and frozen sessions The post 7 Pandas Performance Tricks Every Data Scientist Should Know appeared first on Towards Data Science. Benjamin Nweke Go to original source
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The Machine Learning “Advent Calendar” Day 9: LOF in Excel
The Machine Learning “Advent Calendar” Day 9: LOF in Excel In this article, we explore LOF through three simple steps: distances and neighbors, reachability distances, and the final LOF score. Using tiny datasets, we see how two anomalies can look obvious to us but completely different to different algorithms. This reveals the key idea of…
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How to Develop AI-Powered Solutions, Accelerated by AI
How to Develop AI-Powered Solutions, Accelerated by AI From idea to impact : using AI as your accelerating copilot The post How to Develop AI-Powered Solutions, Accelerated by AI appeared first on Towards Data Science. Anna Via Go to original source
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A Realistic Roadmap to Start an AI Career in 2026
A Realistic Roadmap to Start an AI Career in 2026 How to learn AI in 2026 through real, usable projects The post A Realistic Roadmap to Start an AI Career in 2026 appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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Bridging the Silence: How LEO Satellites and Edge AI Will Democratize Connectivity
Bridging the Silence: How LEO Satellites and Edge AI Will Democratize Connectivity Why on-device intelligence and low-orbit constellations are the only viable path to universal accessibility The post Bridging the Silence: How LEO Satellites and Edge AI Will Democratize Connectivity appeared first on Towards Data Science. Aakash Goswami Go to original source
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The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel
The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel Isolation Forest may look technical, but its idea is simple: isolate points using random splits. If a point is isolated quickly, it is an anomaly; if it takes many splits, it is normal. Using the tiny dataset 1, 2, 3, 9, we can see…
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How to Climb the Hidden Career Ladder of Data Science
How to Climb the Hidden Career Ladder of Data Science The behaviors that get you promoted The post How to Climb the Hidden Career Ladder of Data Science appeared first on Towards Data Science. Greg Rafferty Go to original source
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The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier
The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error. Today, for Day 7 of the Machine Learning “Advent Calendar”, we switch to classification. With just one numerical feature and two classes, we explore how…
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Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained
Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained Understanding AI in 2026 — from machine learning to generative models The post Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor
The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor During the first days of this Machine Learning Advent Calendar, we explored models based on distances. Today, we switch to a completely different way of learning: Decision Trees. With a simple one-feature dataset, we can see how a tree chooses its first split. The idea…
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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…
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A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play
A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play What a simple puzzle game reveals about experimentation, product thinking, and data science The post A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play appeared first on Towards Data Science. Yu Dong Go to original source
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The Machine Learning “Advent Calendar” Day 4: k-Means in Excel
The Machine Learning “Advent Calendar” Day 4: k-Means in Excel How to implement a training algorithm that finally looks like “real” machine learning The post The Machine Learning “Advent Calendar” Day 4: k-Means in Excel appeared first on Towards Data Science. angela shi Go to original source
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Build and Deploy Your First Supply Chain App in 20 Minutes
Build and Deploy Your First Supply Chain App in 20 Minutes A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated with GPT-5.1 by Samir Saci) The post Build and Deploy Your First Supply Chain App in 20 Minutes appeared first on Towards Data Science. Samir Saci Go to original…
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Bootstrap a Data Lakehouse in an Afternoon
Bootstrap a Data Lakehouse in an Afternoon Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB The post Bootstrap a Data Lakehouse in an Afternoon appeared first on Towards Data Science. Thomas Reid Go to original source
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The Best Data Scientists are Always Learning
The Best Data Scientists are Always Learning Why continuous learning matters & how to come up with topics to study The post The Best Data Scientists are Always Learning appeared first on Towards Data Science. Jarom Hulet Go to original source
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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
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The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel
The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel Exploring the k-NN classifier with its variants and improvements The post The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel appeared first on Towards Data Science. angela shi Go to original source
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JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability
JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability Benchmarking JSON libraries for large payloads The post JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability appeared first on Towards Data Science. Subha Ganapathi Go to original source
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How to Use Simple Data Contracts in Python for Data Scientists
How to Use Simple Data Contracts in Python for Data Scientists Stop your pipelines from breaking on Friday afternoons using simple, open-source validation with Pandera. The post How to Use Simple Data Contracts in Python for Data Scientists appeared first on Towards Data Science. Eirik Berge Go to original source
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month Christmas connections, Copilot’s costs, careful (no-)choices The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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The Machine Learning “Advent Calendar” Day 1: k-NN Regressor in Excel
The Machine Learning “Advent Calendar” Day 1: k-NN Regressor in Excel This first day of the Advent Calendar introduces the k-NN regressor, the simplest distance-based model. Using Excel, we explore how predictions rely entirely on the closest observations, why feature scaling matters, and how heterogeneous variables can make distances meaningless. Through examples with continuous and…
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The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint
The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint Opening the black box of ML models, step by step, directly in Excel The post The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint appeared first on Towards Data Science. angela shi Go to original source
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The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall
The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall A modification to the Boruta algorithm that dramatically reduces computation while maintaining high sensitivity The post The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall appeared first on Towards Data Science. Nicolas Vana Go to original source
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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
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Data Science in 2026: Is It Still Worth It?
Data Science in 2026: Is It Still Worth It? An honest view from a 10-year AI Engineer The post Data Science in 2026: Is It Still Worth It? appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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The Product Health Score: How I Reduced Critical Incidents by 35% with Unified Monitoring and n8n Automation
The Product Health Score: How I Reduced Critical Incidents by 35% with Unified Monitoring and n8n Automation How product, growth and engineering teams can converge on a single signal for better incident management The post The Product Health Score: How I Reduced Critical Incidents by 35% with Unified Monitoring and n8n Automation appeared first on…
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Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That
Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That From insurance premiums to courtrooms: the impact of noise The post Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That appeared first on Towards Data Science. Sean Moran Go to original source
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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…
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RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar
RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar The high-resolution physics turning microwave echoes into real-time flood intelligence The post RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar appeared first on Towards Data Science. Aakash Goswami Go to original source
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How to Implement Three Use Cases for the New Calendar-Based Time Intelligence
How to Implement Three Use Cases for the New Calendar-Based Time Intelligence Starting with the September 2025 Release of Power BI, Microsoft introduced the new Calendar-based Time Intelligence feature. Let’s see what can be done by implementing three use cases. The future looks very interesting with this new feature. The post How to Implement Three…
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How to Implement Randomization with the Python Random Module
How to Implement Randomization with the Python Random Module Let’s generate randomness in our code’s outputs The post How to Implement Randomization with the Python Random Module appeared first on Towards Data Science. Mahnoor Javed Go to original source
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Struggling with Data Science? 5 Common Beginner Mistakes
Struggling with Data Science? 5 Common Beginner Mistakes Avoid these mistakes to fast track your data science career. The post Struggling with Data Science? 5 Common Beginner Mistakes appeared first on Towards Data Science. Egor Howell Go to original source
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Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series
Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series A step-by-step breakdown of empirical mode decomposition to help you extract patterns from time series The post Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series appeared first on Towards Data Science. Sabrine Bendimerad Go to…
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Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off
Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off The best models live in the sweet spot: generalizing well, learning enough, but not too much The post Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off appeared first on Towards Data Science. Frida Karvouni Go to original source
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Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB
Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB How I learned to handle growing datasets without slowing down my entire workflow The post Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB appeared first on Towards Data Science. Benjamin Nweke Go to original source
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How To Build a Graph-Based Recommendation Engine Using EDG and Neo4j
How To Build a Graph-Based Recommendation Engine Using EDG and Neo4j Use a shared taxonomy to connect RDF and property graphs—and power smarter recommendations with inferencing The post How To Build a Graph-Based Recommendation Engine Using EDG and Neo4j appeared first on Towards Data Science. Steve Hedden Go to original source
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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
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TDS Newsletter: How to Build Robust Data and AI Systems
TDS Newsletter: How to Build Robust Data and AI Systems Many practitioners like to jump headfirst into the nitty-gritty details of implementing AI-powered tools. We get it: tinkering your way into a solution can sometimes save you time, and it’s often a fun way to go about learning. As the articles we’re highlighting this week show,…
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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…
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Why I’m Making the Switch to marimo Notebooks
Why I’m Making the Switch to marimo Notebooks A fresh way to think about computational notebooks The post Why I’m Making the Switch to marimo Notebooks appeared first on Towards Data Science. Parul Pandey Go to original source
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PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch
PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch Hands-on PyTorch: Building a 3-layer neural network for multiple regression The post PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch appeared first on Towards Data Science. Gustavo Santos Go to original source
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Why LLMs Aren’t a One-Size-Fits-All Solution for Enterprises
Why LLMs Aren’t a One-Size-Fits-All Solution for Enterprises LLMs are a seamless way to find value in your unstructured data, but the truth is, there is so much more value hidden within your structured data. This post explores what LLMs are (and aren’t) optimized for and how the industry is approaching AI over structured business…
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Introducing Google’s File Search Tool
Introducing Google’s File Search Tool The search giant fires its latest salvo against traditional RAG processing. The post Introducing Google’s File Search Tool appeared first on Towards Data Science. Thomas Reid Go to original source
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Introducing ShaTS: A Shapley-Based Method for Time-Series Models
Introducing ShaTS: A Shapley-Based Method for Time-Series Models Why you should not explain your time-series data with tabular Shapley methods The post Introducing ShaTS: A Shapley-Based Method for Time-Series Models appeared first on Towards Data Science. Manuel Franco de la Peña Go to original source
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The Absolute Beginner’s Guide to Pandas DataFrames
The Absolute Beginner’s Guide to Pandas DataFrames Learn how to initialize dataframes from dictionaries, lists, and NumPy arrays The post The Absolute Beginner’s Guide to Pandas DataFrames appeared first on Towards Data Science. Ibrahim Salami Go to original source
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How to Automate Workflows with AI
How to Automate Workflows with AI Learn how to take a manual process and optimize it using AI The post How to Automate Workflows with AI appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Organizing Code, Experiments, and Research for Kaggle Competitions
Organizing Code, Experiments, and Research for Kaggle Competitions Lessons and tips learned while earning a Kaggle Competition Medal The post Organizing Code, Experiments, and Research for Kaggle Competitions appeared first on Towards Data Science. Ibrahim Habib Go to original source
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Spearman Correlation Coefficient for When Pearson Isn’t Enough
Spearman Correlation Coefficient for When Pearson Isn’t Enough Not all relationships are linear, and that is where Spearman comes in. The post Spearman Correlation Coefficient for When Pearson Isn’t Enough appeared first on Towards Data Science. Nikhil Dasari Go to original source
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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
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The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or an LLM (Explained with One Example)
The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or an LLM (Explained with One Example) A practical use case to describe how the data scientist job changed across three generations of machine learning The post The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning,…
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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
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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
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Data Culture Is the Symptom, Not the Solution
Data Culture Is the Symptom, Not the Solution The hidden reason your data investments fail The post Data Culture Is the Symptom, Not the Solution appeared first on Towards Data Science. Jens Linden Go to original source
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LLM-Powered Time-Series Analysis
LLM-Powered Time-Series Analysis Part 2: Prompts for Advanced Model Development The post LLM-Powered Time-Series Analysis appeared first on Towards Data Science. Sara Nobrega Go to original source
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Power Analysis in Marketing: A Hands-On Introduction
Power Analysis in Marketing: A Hands-On Introduction Part 1: What is statistical power and how do we compute it? The post Power Analysis in Marketing: A Hands-On Introduction appeared first on Towards Data Science. Sam Arrington Go to original source
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Evaluating Synthetic Data — The Million Dollar Question
Evaluating Synthetic Data — The Million Dollar Question Learn how to evaluate synthetic data quality using the Maximum Similarity Test — a simple, quantitative approach for assessing fidelity, utility, and privacy in synthetic datasets. The post Evaluating Synthetic Data — The Million Dollar Question appeared first on Towards Data Science. Andrew Skabar Go to original…
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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…
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AI Papers to Read in 2025
AI Papers to Read in 2025 Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science. The post AI Papers to Read in 2025 appeared first on Towards Data Science. Ygor Serpa Go to original source
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Why Nonparametric Models Deserve a Second Look
Why Nonparametric Models Deserve a Second Look Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms. The post Why Nonparametric Models Deserve a Second Look appeared first on Towards Data Science. Andrew Skabar Go to original source
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NumPy for Absolute Beginners: A Project-Based Approach to Data Analysis
NumPy for Absolute Beginners: A Project-Based Approach to Data Analysis Build a high-performance sensor data pipeline from scratch and unlock the true speed of Python’s scientific computing core The post NumPy for Absolute Beginners: A Project-Based Approach to Data Analysis appeared first on Towards Data Science. Ibrahim Salami Go to original source
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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
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What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later
What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later Here’s why it happens — and how to fix it The post What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later appeared first on Towards Data Science. Javier Marin Go to original source
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From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers
From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers From ARIMA to N-BEATS: Comparing forecasting approaches that balance accuracy, interpretability, and sustainability The post From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers appeared first on Towards Data Science. Dr. Theophano Mitsa Go…
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The Pearson Correlation Coefficient, Explained Simply
The Pearson Correlation Coefficient, Explained Simply A simple explanation of the Pearson correlation coefficient with examples The post The Pearson Correlation Coefficient, Explained Simply appeared first on Towards Data Science. Nikhil Dasari Go to original source
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The Machine Learning Projects Employers Want to See
The Machine Learning Projects Employers Want to See What machine learning projects will actually get you interviews and jobs The post The Machine Learning Projects Employers Want to See appeared first on Towards Data Science. Egor Howell Go to original source
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Building a Rules Engine from First Principles
Building a Rules Engine from First Principles How recasting propositional logic as sparse algebra leads to an elegant and efficient design The post Building a Rules Engine from First Principles appeared first on Towards Data Science. Dmitry Lesnik Go to original source
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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
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Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs
Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs Understanding how AI models “reason” and why it’s not what humans do when we think The post Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs appeared first on Towards Data Science.…
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A Real-World Example of Using UDF in DAX
A Real-World Example of Using UDF in DAX With the September 2025 release of Power BI, we get the new user-defined function feature. This is an excellent addition to our toolset. Let’s see how to build a real-world example of this new feature. The post A Real-World Example of Using UDF in DAX appeared first…
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month October 2025: READMEs, MIGs, and movements The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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Building a Monitoring System That Actually Works
Building a Monitoring System That Actually Works A step-by-step guide to catching real anomalies without drowning in false alerts The post Building a Monitoring System That Actually Works appeared first on Towards Data Science. Mariya Mansurova Go to original source
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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
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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
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Building a Geospatial Lakehouse with Open Source and Databricks
Building a Geospatial Lakehouse with Open Source and Databricks An example workflow for vector geospatial data science The post Building a Geospatial Lakehouse with Open Source and Databricks appeared first on Towards Data Science. Robert Constable Go to original source
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When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation
When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation Exploring the frequency fingerprints of Transformers to guide smarter knowledge distillation The post When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation appeared first on Towards Data Science. Ankit Singh Chauhan Go to original source
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Multiple Linear Regression Explained Simply (Part 1)
Multiple Linear Regression Explained Simply (Part 1) The math behind fitting a plane instead of a line. The post Multiple Linear Regression Explained Simply (Part 1) appeared first on Towards Data Science. Nikhil Dasari Go to original source
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Why Should We Bother with Quantum Computing in ML?
Why Should We Bother with Quantum Computing in ML? Quantum Machine Learning principles The post Why Should We Bother with Quantum Computing in ML? appeared first on Towards Data Science. Erika G. Gonçalves Go to original source