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Tag: data

  • When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

    When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts arXiv:2507.14661v1 Announce Type: new Abstract: Semi-supervised domain adaptation (SSDA) aims to achieve high predictive performance in the target domain with limited labeled target data by exploiting abundant source and unlabeled target data. Despite its significance in numerous…

  • Conformal Data Contamination Tests for Trading or Sharing of Data

    Conformal Data Contamination Tests for Trading or Sharing of Data arXiv:2507.13835v1 Announce Type: new Abstract: The amount of quality data in many machine learning tasks is limited to what is available locally to data owners. The set of quality data can be expanded through trading or sharing with external data agents. However, data buyers need…

  • Company Killed University Programs

    Company Killed University Programs Normally, I would have a post around this time hyping up fall recruiting and trying to provide pointers. The company I work for has decided to hire no additional entry level data scientists this year outside of intern return offers. They have also cut the number of intern positions in half…

  • Generating random noise for media data

    Generating random noise for media data Hey everyone – I work on an ML team in the industry, and I’m currently building a predictive model to catch signals in live media data to sense when potential viral moments or crises are happening for brands. We have live media trackers at my company that capture all…

  • How would you structure a project (data frame) to scrape and track listing changes over time?

    How would you structure a project (data frame) to scrape and track listing changes over time? I’m working on a project where I want to scrape data daily (e.g., real estate listings from a site like RentFaster or Zillow) and track how each listing changes over time. I want to be able to answer questions…

  • Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2)

    Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2) Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2) appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source

  • Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?

    Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work? arXiv:2507.11891v1 Announce Type: new Abstract: We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has shown that the standard difference-in-means estimator is biased in estimating the global treatment effect (GTE) due to a particular…

  • What Can the History of Data Tell Us About the Future of AI?

    What Can the History of Data Tell Us About the Future of AI? A 40-Year Look at Data, Business Models, and the Forces Shaping Intelligent Systems The post What Can the History of Data Tell Us About the Future of AI? appeared first on Towards Data Science. Steve Hedden Go to original source

  • Reducing Time to Value for Data Science Projects: Part 3

    Reducing Time to Value for Data Science Projects: Part 3 Setting up a robust experimentation process The post Reducing Time to Value for Data Science Projects: Part 3 appeared first on Towards Data Science. Kristopher McGlinchey Go to original source

  • Work Data Is the Next Frontier for GenAI

    Work Data Is the Next Frontier for GenAI 9 reasons why work data is the single most valuable data source for LLM training, uniquely capable of propelling LLM performance to unprecedented heights. The post Work Data Is the Next Frontier for GenAI appeared first on Towards Data Science. Zsombor Varnagy-Toth 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

  • What I Learned in my First 18 Months as a Freelance Data Scientist

    What I Learned in my First 18 Months as a Freelance Data Scientist The taxes and health insurance edition The post What I Learned in my First 18 Months as a Freelance Data Scientist appeared first on Towards Data Science. CJ Sullivan Go to original source

  • Rethinking Data Science Interviews in the Age of AI

    Rethinking Data Science Interviews in the Age of AI How AI is transforming data science interviews—and what hiring managers and candidates should do to adapt The post Rethinking Data Science Interviews in the Age of AI appeared first on Towards Data Science. Yu Dong Go to original source

  • Change-Aware Data Validation with Column-Level Lineage

    Change-Aware Data Validation with Column-Level Lineage Data transformation tools like dbt make constructing SQL data pipelines easy and systematic. But even with the added structure and clearly defined data models, pipelines can still become complex, which makes debugging issues and validating changes to data models difficult. The post Change-Aware Data Validation with Column-Level Lineage appeared…

  • Hybrid least squares for learning functions from highly noisy data

    Hybrid least squares for learning functions from highly noisy data arXiv:2507.02215v1 Announce Type: new Abstract: Motivated by the need for efficient estimation of conditional expectations, we consider a least-squares function approximation problem with heavily polluted data. Existing methods that are powerful in the small noise regime are suboptimal when large noise is present. We propose…

  • When Less Is More: Binary Feedback Can Outperform Ordinal Comparisons in Ranking Recovery

    When Less Is More: Binary Feedback Can Outperform Ordinal Comparisons in Ranking Recovery arXiv:2507.01613v1 Announce Type: new Abstract: Paired comparison data, where users evaluate items in pairs, play a central role in ranking and preference learning tasks. While ordinal comparison data intuitively offer richer information than binary comparisons, this paper challenges that conventional wisdom. We…

  • 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

  • How to Access NASA’s Climate Data — And How It’s Powering the Fight Against Climate Change Pt. 1

    How to Access NASA’s Climate Data — And How It’s Powering the Fight Against Climate Change Pt. 1 From architectural design to food security. The post How to Access NASA’s Climate Data — And How It’s Powering the Fight Against Climate Change Pt. 1 appeared first on Towards Data Science. Marco Hening Tallarico Go to…

  • Become a Better Data Scientist with These Prompt Engineering Tips and Tricks

    Become a Better Data Scientist with These Prompt Engineering Tips and Tricks Part 1: prompt engineering for planning, cleaning, and EDA The post Become a Better Data Scientist with These Prompt Engineering Tips and Tricks appeared first on Towards Data Science. Sara Nobrega Go to original source

  • A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline

    A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline PyTorch model performance analysis and optimization — Part 8 The post A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline appeared first on Towards Data Science. Chaim Rand Go to original source

  • Data Science: From School to Work, Part V

    Data Science: From School to Work, Part V How to profile your Python project The post Data Science: From School to Work, Part V appeared first on Towards Data Science. Vincent Margot Go to original source

  • The Mythical Pivot Point from Buy to Build for Data Platforms

    The Mythical Pivot Point from Buy to Build for Data Platforms For companies with data-intensive architectures, there often comes a pivotal point where building in-house data platforms makes more sense than buying off-the-shelf solutions The post The Mythical Pivot Point from Buy to Build for Data Platforms appeared first on Towards Data Science. Ming Gao…

  • Data-Driven Dynamic Factor Modeling via Manifold Learning

    Data-Driven Dynamic Factor Modeling via Manifold Learning arXiv:2506.19945v1 Announce Type: new Abstract: We propose a data-driven dynamic factor framework where a response variable depends on a high-dimensional set of covariates, without imposing any parametric model on the joint dynamics. Leveraging Anisotropic Diffusion Maps, a nonlinear manifold learning technique introduced by Singer and Coifman, our framework…

  • How to Train a Chatbot Using RAG and Custom Data

    How to Train a Chatbot Using RAG and Custom Data Retrieval-Augmented Generation made easy with Llama The post How to Train a Chatbot Using RAG and Custom Data appeared first on Towards Data Science. Haden Pelletier Go to original source

  • Data Has No Moat!

    Data Has No Moat! Only if you ignore data quality The post Data Has No Moat! appeared first on Towards Data Science. Fabiana Clemente Go to original source

  • [Project] I just open-sourced a plugin to stop AI from hallucinating your schemas

    [Project] I just open-sourced a plugin to stop AI from hallucinating your schemas Hey r/datascience 👋 Using AI tools like Copilot or Cursor can be a total headache for data science work. You’re trying to join tables, and it confidently suggests customer_id when your table actually uses cust_pk. Or worse, it just invents tables that…

  • Rademacher learning rates for iterated random functions

    Rademacher learning rates for iterated random functions arXiv:2506.13946v1 Announce Type: new Abstract: Most existing literature on supervised machine learning assumes that the training dataset is drawn from an i.i.d. sample. However, many real-world problems exhibit temporal dependence and strong correlations between the marginal distributions of the data-generating process, suggesting that the i.i.d. assumption is often…

  • Abstract Classes: A Software Engineering Concept Data Scientists Must Know To Succeed

    Abstract Classes: A Software Engineering Concept Data Scientists Must Know To Succeed Simple concepts that differentiate a professional from amateurs. The post Abstract Classes: A Software Engineering Concept Data Scientists Must Know To Succeed appeared first on Towards Data Science. Benjamin Lee Go to original source

  • Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project

    Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project Three cases to use the Sphinx tool as a pro The post Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project appeared first on Towards Data Science. Radmila Mandzhieva Go to original source

  • Build an AI Agent to Explore Your Data Catalog with Natural Language

    Build an AI Agent to Explore Your Data Catalog with Natural Language Leverage LLMs to query your Databricks Data Catalog The post Build an AI Agent to Explore Your Data Catalog with Natural Language appeared first on Towards Data Science. Fabiana Clemente Go to original source

  • Don’t be the data scientist who’s in love with models, be the one who solves real problems

    Don’t be the data scientist who’s in love with models, be the one who solves real problems work at a company with around 100 data scientists, ML and data engineers. The most frustrating part of working with many data scientists and honestly, I see this on this sub all the time too, is how obsessed…

  • “Data Annotation” spam

    “Data Annotation” spam Anyone else’s job search site just absolutely spammed by Data Annotation? If I look up Data, ML, AI, or anything similar in my area I get 2-3 pages of there job posting. submitted by /u/MahaloMerky [link] [comments] /u/MahaloMerky 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 to Transition From Data Analyst to Data Scientist

    How to Transition From Data Analyst to Data Scientist Playbook on how data analysts can become data scientists The post How to Transition From Data Analyst to Data Scientist appeared first on Towards Data Science. Egor Howell Go to original source

  • PhD vs Masters prepared data scientist expectations.

    PhD vs Masters prepared data scientist expectations. Is there anything more that you expect from a data scientist with a PhD versus a data scientist with just a master’s degree, given the same level of experience? For the companies that I’ve worked with, most data science teams were mixes of folks with master’s degrees and…

  • Data analyst vs. engineer? At non-profit

    Data analyst vs. engineer? At non-profit Hi all, I am the only Data Analyst at a medium-sized company related to shared transportation (adjacent to Lime Scooter/Bike). I’m pretty early in my career (grad from college 3 years ago). My role encompasses a LOT of responsibilities that aren’t traditionally under “data analyst”, the biggest of which…

  • Nonlinear Causal Discovery for Grouped Data

    Nonlinear Causal Discovery for Grouped Data arXiv:2506.05120v1 Announce Type: new Abstract: Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather…

  • Assumption-free stability for ranking problems

    Assumption-free stability for ranking problems arXiv:2506.02257v1 Announce Type: new Abstract: In this work, we consider ranking problems among a finite set of candidates: for instance, selecting the top-$k$ items among a larger list of candidates or obtaining the full ranking of all items in the set. These problems are often unstable, in the sense that…

  • Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is

    Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is Monitoring is easy; what to monitor is not. In the field of machine learning, data drift is just noise until you know what it means. The post Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is appeared first on Towards Data Science.…

  • Reducing Time to Value for Data Science Projects: Part 2

    Reducing Time to Value for Data Science Projects: Part 2 Leveraging automation and parallelism to scale out experiments The post Reducing Time to Value for Data Science Projects: Part 2 appeared first on Towards Data Science. Kristopher McGlinchey Go to original source

  • Decision Trees Natively Handle Categorical Data

    Decision Trees Natively Handle Categorical Data But mean target encoding is their turbocharger The post Decision Trees Natively Handle Categorical Data appeared first on Towards Data Science. Vadim Arzamasov Go to original source

  • Overfitting has a limitation: a model-independent generalization error bound based on R’enyi entropy

    Overfitting has a limitation: a model-independent generalization error bound based on R’enyi entropy arXiv:2506.00182v1 Announce Type: new Abstract: Will further scaling up of machine learning models continue to bring success? A significant challenge in answering this question lies in understanding generalization error, which is the impact of overfitting. Understanding generalization error behavior of increasingly large-scale…

  • Bayesian Data Sketching for Varying Coefficient Regression Models

    Bayesian Data Sketching for Varying Coefficient Regression Models arXiv:2506.00270v1 Announce Type: new Abstract: Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received limited attention in large data applications, primarily due to prohibitively slow posterior computations using Markov chain Monte Carlo (MCMC) algorithms. We introduce Bayesian…

  • A Mathematical Perspective On Contrastive Learning

    A Mathematical Perspective On Contrastive Learning arXiv:2505.24134v1 Announce Type: new Abstract: Multimodal contrastive learning is a methodology for linking different data modalities; the canonical example is linking image and text data. The methodology is typically framed as the identification of a set of encoders, one for each modality, that align representations within a common latent…

  • Can data science be used in computer networking (if not can it be used in cybersecurity)?

    Can data science be used in computer networking (if not can it be used in cybersecurity)? Hi, I’m a high schooler (junior year) who is extremely interested in data science to the point where it is the main career field I want to go into. However, I got enrolled in a program where we train…

  • The Secret Power of Data Science in Customer Support

    The Secret Power of Data Science in Customer Support Customer support is a data goldmine. Here’s how to unlock its full potential with data science. The post The Secret Power of Data Science in Customer Support appeared first on Towards Data Science. Yu Dong Go to original source

  • I Transitioned from Data Science to AI Engineering: Here’s Everything You Need to Know

    I Transitioned from Data Science to AI Engineering: Here’s Everything You Need to Know A personal guide to the skills, tools, and mindset behind the title The post I Transitioned from Data Science to AI Engineering: Here’s Everything You Need to Know appeared first on Towards Data Science. Sara Nobrega Go to original source

  • From Data to Stories: Code Agents for KPI Narratives

    From Data to Stories: Code Agents for KPI Narratives HuggingFace’s smolagents framework in action The post From Data to Stories: Code Agents for KPI Narratives appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Learning with Expected Signatures: Theory and Applications

    Learning with Expected Signatures: Theory and Applications arXiv:2505.20465v1 Announce Type: new Abstract: The expected signature maps a collection of data streams to a lower dimensional representation, with a remarkable property: the resulting feature tensor can fully characterize the data generating distribution. This “model-free” embedding has been successfully leveraged to build multiple domain-agnostic machine learning (ML)…

  • Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models

    Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models arXiv:2505.20536v1 Announce Type: new Abstract: This paper studies the task of estimating heterogeneous treatment effects in causal panel data models, in the presence of covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) for panel data models, that employs flexible model structures and powerful…

  • 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

  • How to Generate Synthetic Data: A Comprehensive Guide Using Bayesian Sampling and Univariate Distributions

    How to Generate Synthetic Data: A Comprehensive Guide Using Bayesian Sampling and Univariate Distributions Data makes the engine run in many organisations. But what if the number of observations is too low or there is only expert knowledge? I will demonstrate how to generate synthetic data with applications in predictive maintenance. The post How to…

  • Learning Probabilities of Causation from Finite Population Data

    Learning Probabilities of Causation from Finite Population Data arXiv:2505.17133v1 Announce Type: new Abstract: Probabilities of causation play a crucial role in modern decision-making. This paper addresses the challenge of predicting probabilities of causation for subpopulations with textbf{insufficient} data using machine learning models. Tian and Pearl first defined and derived tight bounds for three fundamental probabilities…

  • Is studying Data Science still worth it?

    Is studying Data Science still worth it? Hi everyone, I’m currently studying data science, but I’ve been hearing that the demand for data scientists is decreasing significantly. I’ve also been told that many data scientists are essentially becoming analysts, while the machine learning side of things is increasingly being handled by engineers. Does it still…

  • Inheritance: A Software Engineering Concept Data Scientists Must Know To Succeed

    Inheritance: A Software Engineering Concept Data Scientists Must Know To Succeed Coding concepts that distinguish an amateur from a professional data scientist The post Inheritance: A Software Engineering Concept Data Scientists Must Know To Succeed appeared first on Towards Data Science. Benjamin Lee Go to original source

  • A Linear Approach to Data Poisoning

    A Linear Approach to Data Poisoning arXiv:2505.15175v1 Announce Type: new Abstract: We investigate the theoretical foundations of data poisoning attacks in machine learning models. Our analysis reveals that the Hessian with respect to the input serves as a diagnostic tool for detecting poisoning, exhibiting spectral signatures that characterize compromised datasets. We use random matrix theory…

  • Top Machine Learning Jobs and How to Prepare For Them

    Top Machine Learning Jobs and How to Prepare For Them These days, job titles like data scientist, machine learning engineer, and Ai Engineer are everywhere — and if you were anything like me, it can be hard to understand what each of them actually does if you are not working within the field. And then there are titles…

  • Data Balancing Strategies: A Survey of Resampling and Augmentation Methods

    Data Balancing Strategies: A Survey of Resampling and Augmentation Methods arXiv:2505.13518v1 Announce Type: new Abstract: Imbalanced data poses a significant obstacle in machine learning, as an unequal distribution of class labels often results in skewed predictions and diminished model accuracy. To mitigate this problem, various resampling strategies have been developed, encompassing both oversampling and undersampling…

  • 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…

  • Missing Data Imputation by Reducing Mutual Information with Rectified Flows

    Missing Data Imputation by Reducing Mutual Information with Rectified Flows arXiv:2505.11749v1 Announce Type: new Abstract: This paper introduces a novel iterative method for missing data imputation that sequentially reduces the mutual information between data and their corresponding missing mask. Inspired by GAN-based approaches, which train generators to decrease the predictability of missingness patterns, our method…

  • The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated

    The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated The saying goes that 80% of data collected, stored and maintained by governments can be associated with geographical locations. Although never empirically proven, it illustrates the importance of location within data. Ever growing data volumes put constraints on systems that handle geospatial data. Common Big…

  • Parquet File Format – Everything You Need to Know!

    Parquet File Format – Everything You Need to Know! With the amount of Data growing exponentially in the last few years, one of the biggest challenges has become finding the most optimal way to store various data flavors. Unlike in the (not so far) past, when relational databases were considered the only way to go,…

  • Time Series Forecasting Made Simple (Part 2): Customizing Baseline Models

    Time Series Forecasting Made Simple (Part 2): Customizing Baseline Models Thank you for the kind response to Part 1, it’s been encouraging to see so many readers interested in time series forecasting. In Part 1 of this series, we broke down time series data into trend, seasonality, and noise, discussed when to use additive versus…

  • Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data

    Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data arXiv:2505.04954v1 Announce Type: new Abstract: The identification of a linear system model from data has wide applications in control theory. The existing work that provides finite sample guarantees for linear system identification typically uses data from a single long system trajectory under i.i.d.…

  • Boosting Statistic Learning with Synthetic Data from Pretrained Large Models

    Boosting Statistic Learning with Synthetic Data from Pretrained Large Models arXiv:2505.04992v1 Announce Type: new Abstract: The rapid advancement of generative models, such as Stable Diffusion, raises a key question: how can synthetic data from these models enhance predictive modeling? While they can generate vast amounts of datasets, only a subset meaningfully improves performance. We propose…

  • 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,…

  • Generating Data Dictionary for Excel Files Using OpenPyxl and AI Agents

    Generating Data Dictionary for Excel Files Using OpenPyxl and AI Agents Introduction Every company I worked for until today, there it was: the resilient MS Excel. Excel was first released in 1985 and has remained strong until today. It has survived the rise of relational databases, the evolution of many programming languages, the Internet with…

  • Generate-then-Verify: Reconstructing Data from Limited Published Statistics

    Generate-then-Verify: Reconstructing Data from Limited Published Statistics arXiv:2504.21199v1 Announce Type: new Abstract: We study the problem of reconstructing tabular data from aggregate statistics, in which the attacker aims to identify interesting claims about the sensitive data that can be verified with 100% certainty given the aggregates. Successful attempts in prior work have conducted studies in…

  • Learning and Generalization with Mixture Data

    Learning and Generalization with Mixture Data arXiv:2504.20651v1 Announce Type: new Abstract: In many, if not most, machine learning applications the training data is naturally heterogeneous (e.g. federated learning, adversarial attacks and domain adaptation in neural net training). Data heterogeneity is identified as one of the major challenges in modern day large-scale learning. A classical way…

  • Data Analyst or Data Engineer or Analytics Engineer or BI Engineer ?

    Data Analyst or Data Engineer or Analytics Engineer or BI Engineer ? If you’ve followed me for a while, you probably know I started my career as a QA engineer before transitioning into the world of data analytics. I didn’t go to school for it, didn’t have a mentor, and didn’t land in a formal training…

  • When OpenAI Isn’t Always the Answer: Enterprise Risks Behind Wrapper-Based AI Agents

    When OpenAI Isn’t Always the Answer: Enterprise Risks Behind Wrapper-Based AI Agents “Wait… are you sending journal entries to OpenAI?” That was the first thing my friend asked when I showed her Feel-Write, an AI-powered journaling app I built during a hackathon in San Francisco. I shrugged. “It was an AI-themed hackathon, I had to…

  • Towards Accurate Forecasting of Renewable Energy : Building Datasets and Benchmarking Machine Learning Models for Solar and Wind Power in France

    Towards Accurate Forecasting of Renewable Energy : Building Datasets and Benchmarking Machine Learning Models for Solar and Wind Power in France arXiv:2504.16100v1 Announce Type: cross Abstract: Accurate prediction of non-dispatchable renewable energy sources is essential for grid stability and price prediction. Regional power supply forecasts are usually indirect through a bottom-up approach of plant-level forecasts,…

  • Explainable Unsupervised Anomaly Detection with Random Forest

    Explainable Unsupervised Anomaly Detection with Random Forest arXiv:2504.16075v1 Announce Type: new Abstract: We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real data and synthetic data sampled from a uniform distribution over the real data bounds, a distance measure…

  • How to Get Performance Data from Power BI with DAX Studio

    How to Get Performance Data from Power BI with DAX Studio Introduction To put things straight: I will not discuss how to optimize DAX Code today. More articles will follow, concentrating on common mistakes and how to avoid them. But, before we can understand the performance metrics, we need to understand the architecture of the…

  • MapReduce: How It Powers Scalable Data Processing

    MapReduce: How It Powers Scalable Data Processing In this article, I’ll give a brief introduction to the MapReduce programming model. Hopefully after reading this, you leave with a solid intuition of what MapReduce is, the role it plays in scalable data processing, and how to recognize when it can be applied to optimize a computational…

  • Building a Personal API for Your Data Projects with FastAPI

    Building a Personal API for Your Data Projects with FastAPI How many times have you had a messy Jupyter Notebook filled with copy-pasted code just to re-use some data wrangling logic? Whether you do it for passion or for work, if you code a lot, then you’ve probably answered something like “way too many”. You’re…

  • Beginner’s Guide to Creating a S3 Storage on AWS

    Beginner’s Guide to Creating a S3 Storage on AWS Introduction AWS is a well-known cloud provider whose primary goal is to allocate server resources for software engineers to deploy their applications. AWS offers many services, one of which is EC2, providing virtual machines for running software applications in the cloud. However, for data-intensive applications, storing…

  • Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations

    Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations arXiv:2504.11610v1 Announce Type: new Abstract: Background: The integration and analysis of multi-modal data are increasingly essential across various domains including bioinformatics. As the volume and complexity of such data grow, there is a pressing need for computational models that not only…

  • Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling

    Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling arXiv:2504.10612v1 Announce Type: cross Abstract: Generative models often map noise to data by matching flows or scores, but these approaches become cumbersome for incorporating partial observations or additional priors. Inspired by recent advances in Wasserstein gradient flows, we propose Energy Matching, a framework that…

  • 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…

  • An Incremental Non-Linear Manifold Approximation Method

    An Incremental Non-Linear Manifold Approximation Method arXiv:2504.09068v1 Announce Type: new Abstract: Analyzing high-dimensional data presents challenges due to the “curse of dimensionality”, making computations intensive. Dimension reduction techniques, categorized as linear or non-linear, simplify such data. Non-linear methods are particularly essential for efficiently visualizing and processing complex data structures in interactive and graphical applications. This…

  • An LLM-Based Workflow for Automated Tabular Data Validation 

    An LLM-Based Workflow for Automated Tabular Data Validation  This article is part of a series of articles on automating data cleaning for any tabular dataset: Effortless Spreadsheet Normalisation With LLM You can test the feature described in this article on your own dataset using the CleanMyExcel.io service, which is free and requires no registration. What…

  • Let’s Call a Spade a Spade: RDF and LPG — Cousins Who Should Learn to Live Together

    Let’s Call a Spade a Spade: RDF and LPG — Cousins Who Should Learn to Live Together In recent years, there has been a proliferation of articles, LinkedIn posts, and marketing materials presenting graph data models from different perspectives. This article will refrain from discussing specific products and instead focus solely on the comparison of…

  • Are We Watching More Ads Than Content? Analyzing YouTube Sponsor Data

    Are We Watching More Ads Than Content? Analyzing YouTube Sponsor Data I’m definitely not the only person who feels that YouTube sponsor segments have become longer and more frequent recently. Sometimes, I watch videos that seem to be trying to sell me something every couple of seconds. On one hand, it’s great that both small and…

  • Communication-Efficient l_0 Penalized Least Square

    Communication-Efficient l_0 Penalized Least Square arXiv:2504.00722v1 Announce Type: new Abstract: In this paper, we propose a communication-efficient penalized regression algorithm for high-dimensional sparse linear regression models with massive data. This approach incorporates an optimized distributed system communication algorithm, named CESDAR algorithm, based on the Enhanced Support Detection and Root finding algorithm. The CESDAR algorithm leverages…

  • 4 Levels of GitHub Actions: A Guide to Data Workflow Automation

    4 Levels of GitHub Actions: A Guide to Data Workflow Automation Automation has become an indispensable element for ensuring operational efficiency and reliability in modern software development. GitHub Actions, an integrated Continuous Integration and Continuous Deployment (CI/CD) tool within GitHub, has established its position in the software development industry by providing a comprehensive platform for…

  • Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent

    Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent arXiv:2503.23642v1 Announce Type: new Abstract: We investigate the problem of learning a Single Index Model (SIM)- a popular model for studying the ability of neural networks to learn features – from anisotropic Gaussian inputs by training a neuron using vanilla Stochastic Gradient…

  • A Little More Conversation, A Little Less Action — A Case Against Premature Data Integration

    A Little More Conversation, A Little Less Action — A Case Against Premature Data Integration When I talk to [large] organisations that have not yet properly started with Data Science (DS) and Machine Learning (ML), they often tell me that they have to run a data integration project first, because “…all the data is scattered…

  • Learning Data-Driven Uncertainty Set Partitions for Robust and Adaptive Energy Forecasting with Missing Data

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  • Data-Driven March Madness Predictions

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

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  • Google’s Data Science Agent: Can It Really Do Your Job?

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  • Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems

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  • Six Organizational Models for Data Science

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  • The Hardness of Validating Observational Studies with Experimental Data

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  • Online federated learning framework for classification

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  • Ranking and Selection with Simultaneous Input Data Collection

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  • Learn then Decide: A Learning Approach for Designing Data Marketplaces

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  • Mastering Hadoop, Part 2: Getting Hands-On — Setting Up and Scaling Hadoop

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  • How to Switch from Data Analyst to Data Scientist

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