mailitics

Tag: data

  • Free Learning Paths for Data Analysts, Data Scientists, and Data Engineers – Using 100% Open Resources

    Free Learning Paths for Data Analysts, Data Scientists, and Data Engineers – Using 100% Open Resources Hey, I’m Ryan, and I’ve created https://www.datasciencehive.com/learning-paths A platform offering free, structured learning paths for data enthusiasts and professionals alike. The current paths cover: • Data Analyst: Learn essential skills like SQL, data visualization, and predictive modeling. • Data…

  • 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…

  • 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…

  • Precise asymptotic analysis of Sobolev training for random feature models

    Precise asymptotic analysis of Sobolev training for random feature models arXiv:2511.03050v1 Announce Type: new Abstract: Gradient information is widely useful and available in applications, and is therefore natural to include in the training of neural networks. Yet little is known theoretically about the impact of Sobolev training — regression with both function and gradient data…

  • 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

  • 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

  • Is it too early to accept an internship offer?

    Is it too early to accept an internship offer? I’m a junior studying Data Analytics and Data Engineering at a solid state school. I’ve been a Data Analyst at my university’s career services for the past year, and previously interned as a Data & Business Analytics Intern at a regional credit union. I just got…

  • 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…

  • Bias-Corrected Data Synthesis for Imbalanced Learning

    Bias-Corrected Data Synthesis for Imbalanced Learning arXiv:2510.26046v1 Announce Type: new Abstract: Imbalanced data, where the positive samples represent only a small proportion compared to the negative samples, makes it challenging for classification problems to balance the false positive and false negative rates. A common approach to addressing the challenge involves generating synthetic data for the…

  • Beyond Normality: Reliable A/B Testing with Non-Gaussian Data

    Beyond Normality: Reliable A/B Testing with Non-Gaussian Data arXiv:2510.23666v1 Announce Type: new Abstract: A/B testing has become the cornerstone of decision-making in online markets, guiding how platforms launch new features, optimize pricing strategies, and improve user experience. In practice, we typically employ the pairwise $t$-test to compare outcomes between the treatment and control groups, thereby…

  • What’s next for a 11 YOE data scientist?

    What’s next for a 11 YOE data scientist? Hi folks, Hope you’re having a great day wherever you are in the world. Context: I’ve been in the data science industry for the past 11 years. I started my career in telecom, where I worked extensively on time series analysis and data cleaning using R, Java,…

  • 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

  • Neural Networks for Censored Expectile Regression Based on Data Augmentation

    Neural Networks for Censored Expectile Regression Based on Data Augmentation arXiv:2510.20344v1 Announce Type: new Abstract: Expectile regression neural networks (ERNNs) are powerful tools for capturing heterogeneity and complex nonlinear structures in data. However, most existing research has primarily focused on fully observed data, with limited attention paid to scenarios involving censored observations. In this paper,…

  • Generalization Below the Edge of Stability: The Role of Data Geometry

    Generalization Below the Edge of Stability: The Role of Data Geometry arXiv:2510.18120v1 Announce Type: new Abstract: Understanding generalization in overparameterized neural networks hinges on the interplay between the data geometry, neural architecture, and training dynamics. In this paper, we theoretically explore how data geometry controls this implicit bias. This paper presents theoretical results for overparameterized…

  • 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…

  • A Bayesian Framework for Symmetry Inference in Chaotic Attractors

    A Bayesian Framework for Symmetry Inference in Chaotic Attractors arXiv:2510.16509v1 Announce Type: new Abstract: Detecting symmetry from data is a fundamental problem in signal analysis, providing insight into underlying structure and constraints. When data emerge as trajectories of dynamical systems, symmetries encode structural properties of the dynamics that enable model reduction, principled comparison across conditions,…

  • How I Tailored the Resume That Landed Me $100K+ Data Science and ML Offers

    How I Tailored the Resume That Landed Me $100K+ Data Science and ML Offers How to write a data science and machine learning resume that actually lands jobs. The post How I Tailored the Resume That Landed Me $100K+ Data Science and ML Offers appeared first on Towards Data Science. Egor Howell Go to original…

  • Reliable data clustering with Bayesian community detection

    Reliable data clustering with Bayesian community detection arXiv:2510.15013v1 Announce Type: new Abstract: From neuroscience and genomics to systems biology and ecology, researchers rely on clustering similarity data to uncover modular structure. Yet widely used clustering methods, such as hierarchical clustering, k-means, and WGCNA, lack principled model selection, leaving them susceptible to noise. A common workaround…

  • Conceptual Frameworks for Data Science Projects

    Conceptual Frameworks for Data Science Projects An overview of common framework types and a simple process for building custom frameworks The post Conceptual Frameworks for Data Science Projects appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Machine Learning Meets Panel Data: What Practitioners Need to Know

    Machine Learning Meets Panel Data: What Practitioners Need to Know How to avoid overestimating machine learning models’ performance, usefulness, and real-world applicability due to hidden data leakage The post Machine Learning Meets Panel Data: What Practitioners Need to Know appeared first on Towards Data Science. Marco Letta Go to original source

  • deFOREST: Fusing Optical and Radar satellite data for Enhanced Sensing of Tree-loss

    deFOREST: Fusing Optical and Radar satellite data for Enhanced Sensing of Tree-loss arXiv:2510.14092v1 Announce Type: new Abstract: In this paper we develop a deforestation detection pipeline that incorporates optical and Synthetic Aperture Radar (SAR) data. A crucial component of the pipeline is the construction of anomaly maps of the optical data, which is done using…

  • Conformal Inference for Open-Set and Imbalanced Classification

    Conformal Inference for Open-Set and Imbalanced Classification arXiv:2510.13037v1 Announce Type: new Abstract: This paper presents a conformal prediction method for classification in highly imbalanced and open-set settings, where there are many possible classes and not all may be represented in the data. Existing approaches require a finite, known label space and typically involve random sample…

  • First Principles Thinking for Data Scientists

    First Principles Thinking for Data Scientists The mindset that turns good data scientists into great ones The post First Principles Thinking for Data Scientists appeared first on Towards Data Science. Greg Rafferty Go to original source

  • Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation

    Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation arXiv:2510.09908v1 Announce Type: new Abstract: The rise of large-scale pretrained models has made it feasible to generate predictive or synthetic features at low cost, raising the question of how to incorporate such surrogate predictions into downstream decision-making. We study this problem in the setting of…

  • From data scientist to a new role ?

    From data scientist to a new role ? Hi everyone, I’m 25, currently working as a Data Scientist & AI Engineer at a large Space company in Europe, with ~2.5 years of experience. My focus has been on LLM R&D, RAG pipelines, satellite telemetry anomaly detection, surrogate modeling, and some FPGA-compatible ML for onboard systems.…

  • Free data set that links company to type of activity?

    Free data set that links company to type of activity? Best ressource to classify for example: walmart. food ( top classification) supermarket ( sub classification). I work with european companies also. thanks. submitted by /u/Due-Duty961 [link] [comments] /u/Due-Duty961 Go to original source

  • 10 Data + AI Observations for Fall 2025

    10 Data + AI Observations for Fall 2025 What’s happening—and what’s next— for data and AI at the close of 2025. The post 10 Data + AI Observations for Fall 2025 appeared first on Towards Data Science. Barr Moses Go to original source

  • Past is Prologue: How Conversational Analytics Is Changing Data Work

    Past is Prologue: How Conversational Analytics Is Changing Data Work The future of reporting will be about encoding the value proposition of a product into prompt design. The post Past is Prologue: How Conversational Analytics Is Changing Data Work appeared first on Towards Data Science. Whitney Marks Go to original source

  • How the Rise of Tabular Foundation Models Is Reshaping Data Science

    How the Rise of Tabular Foundation Models Is Reshaping Data Science A turning point for data analysis? The post How the Rise of Tabular Foundation Models Is Reshaping Data Science appeared first on Towards Data Science. Pirmin Lemberger 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

  • The analogy theorem in Hoare logic

    The analogy theorem in Hoare logic arXiv:2510.03685v1 Announce Type: new Abstract: The introduction of machine learning methods has led to significant advances in automation, optimization, and discoveries in various fields of science and technology. However, their widespread application faces a fundamental limitation: the transfer of models between data domains generally lacks a rigorous mathematical justification.…

  • How I Used ChatGPT to Land My Next Data Science Role

    How I Used ChatGPT to Land My Next Data Science Role Practical AI hacks for every stage of the job search  — with real prompts and examples The post How I Used ChatGPT to Land My Next Data Science Role appeared first on Towards Data Science. Yu Dong Go to original source

  • Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide

    Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide Once upon a time, handling streaming data was considered an avant-garde approach. Since the introduction of relational database management systems in the 1970s and traditional data warehousing systems in the late 1980s, all data workloads began and ended with the so-called batch processing. Batch processing relies on the concept of…

  • 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

  • Prediction vs. Search Models: What Data Scientists Are Missing

    Prediction vs. Search Models: What Data Scientists Are Missing How do platform firms set prices and make money? The post Prediction vs. Search Models: What Data Scientists Are Missing appeared first on Towards Data Science. Derek Tran Go to original source

  • Are Foundation Models Ready for Your Production Tabular Data?

    Are Foundation Models Ready for Your Production Tabular Data? A complete review of architectures to make zero-shot predictions in the most common types of datasets. The post Are Foundation Models Ready for Your Production Tabular Data? appeared first on Towards Data Science. Carmen Adriana Martínez Barbosa 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

  • Preparing Video Data for Deep Learning: Introducing Vid Prepper

    Preparing Video Data for Deep Learning: Introducing Vid Prepper A guide to fast video data preprocessing for machine learning The post Preparing Video Data for Deep Learning: Introducing Vid Prepper appeared first on Towards Data Science. Jamie Petherbridge-Conroy Go to original source

  • A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data

    A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data arXiv:2509.20636v1 Announce Type: new Abstract: The analysis of spatial data from biological imaging technology, such as imaging mass spectrometry (IMS) or imaging mass cytometry (IMC), is challenging because of a competitive sampling process which convolves signals from molecules in a single…

  • Is it due to the tech recession?

    Is it due to the tech recession? We know that in many companies Data Scientists are Product Analytics / Data Analysts. I thought it was because MLEs had absorbed the duties of DSs, but i have noticed that this may not be exactly the case. There are basically three distinct roles: Data Analyst / Product…

  • 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

  • From Python to JavaScript: A Playbook for Data Analytics in n8n with Code Node Examples

    From Python to JavaScript: A Playbook for Data Analytics in n8n with Code Node Examples Learn the basics of JavaScript through tiny n8n Code node snippets for sales data analytics The post From Python to JavaScript: A Playbook for Data Analytics in n8n with Code Node Examples appeared first on Towards Data Science. Samir Saci…

  • Has anyone validated synthetic financial data (Gaussian Copula vs CTGAN) in practice?

    Has anyone validated synthetic financial data (Gaussian Copula vs CTGAN) in practice? I’ve been experimenting with generating synthetic datasets for financial indicators (GDP, inflation, unemployment, etc.) and found that CTGAN offered stronger privacy protection in simple linkage tests, but its overall analytical utility was much weaker. In contrast, Gaussian Copula provided reasonably strong privacy and…

  • Database tools and method for tree structured data?

    Database tools and method for tree structured data? I have a database structure which I believe is very common, and very general, so I’m wondering how this is tackled. The database structured like: -> Project (Name of project) -> Category (simple word, ~20 categories) -> Study Study is a directory containing: – README with date…

  • A Focused Approach to Learning SQL

    A Focused Approach to Learning SQL Data is everywhere, but how do you draw insights from it? Often, structured data is stored in relational databases, meaning collections of related tables of data. For instance, a company might store customer purchases in one table, customer demographics in another, and suppliers in a third table. These tables…

  • Scalable extensions to given-data Sobol’ index estimators

    Scalable extensions to given-data Sobol’ index estimators arXiv:2509.09078v1 Announce Type: new Abstract: Given-data methods for variance-based sensitivity analysis have significantly advanced the feasibility of Sobol’ index computation for computationally expensive models and models with many inputs. However, the limitations of existing methods still preclude their application to models with an extremely large number of inputs.…

  • 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

  • PEHRT: A Common Pipeline for Harmonizing Electronic Health Record data for Translational Research

    PEHRT: A Common Pipeline for Harmonizing Electronic Health Record data for Translational Research arXiv:2509.08553v1 Announce Type: new Abstract: Integrative analysis of multi-institutional Electronic Health Record (EHR) data enhances the reliability and generalizability of translational research by leveraging larger, more diverse patient cohorts and incorporating multiple data modalities. However, harmonizing EHR data across institutions poses major…

  • Is Your Training Data Representative? A Guide to Checking with PSI in Python

    Is Your Training Data Representative? A Guide to Checking with PSI in Python Comparing Variable Distributions Between Two Datasets Using Population Stability Index (PSI) and Cramér’s V. The post Is Your Training Data Representative? A Guide to Checking with PSI in Python appeared first on Towards Data Science. JUNIOR JUMBONG Go to original source

  • The End-to-End Data Scientist’s Prompt Playbook

    The End-to-End Data Scientist’s Prompt Playbook Part 3: Prompts for docs, DevOps, and stakeholder communication The post The End-to-End Data Scientist’s Prompt Playbook appeared first on Towards Data Science. Sara Nobrega Go to original source

  • How to evaluate data transformations?

    How to evaluate data transformations? There are several well-established benchmarks for text-to-SQL tasks like BIRD, Spider, and WikiSQL. However, I’m working on a data transformation system that handles per-row transformations with contextual understanding of the input data. The challenge is that most existing benchmarks focus on either: Pure SQL generation (BIRD, Spider) Simple data cleaning…

  • Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows

    Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows A guide to building modular workflows for structured intelligence The post Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows appeared first on Towards Data Science. Subha Ganapathi Go to original source

  • Zero-Inflated Data: A Comparison of Regression Models

    Zero-Inflated Data: A Comparison of Regression Models How to detect it and which model to choose. The post Zero-Inflated Data: A Comparison of Regression Models appeared first on Towards Data Science. Arnaud Capitaine Go to original source

  • Diffusion Generative Models Meet Compressed Sensing, with Applications to Image Data and Financial Time Series

    Diffusion Generative Models Meet Compressed Sensing, with Applications to Image Data and Financial Time Series arXiv:2509.03898v1 Announce Type: new Abstract: This paper develops dimension reduction techniques for accelerating diffusion model inference in the context of synthetic data generation. The idea is to integrate compressed sensing into diffusion models: (i) compress the data into a latent…

  • Scale-Adaptive Generative Flows for Multiscale Scientific Data

    Scale-Adaptive Generative Flows for Multiscale Scientific Data arXiv:2509.02971v1 Announce Type: new Abstract: Flow-based generative models can face significant challenges when modeling scientific data with multiscale Fourier spectra, often producing large errors in fine-scale features. We address this problem within the framework of stochastic interpolants, via principled design of noise distributions and interpolation schedules. The key…

  • Stochastic Differential Equations and Temperature — NASA Climate Data pt. 2

    Stochastic Differential Equations and Temperature — NASA Climate Data pt. 2 The Ornstein-Uhlenbeck process in Python The post Stochastic Differential Equations and Temperature — NASA Climate Data pt. 2 appeared first on Towards Data Science. Marco Hening Tallarico Go to original source

  • What Being a Data Scientist at a Startup Really Looks Like

    What Being a Data Scientist at a Startup Really Looks Like What I learned about growth, visibility, and chaos over the past five years The post What Being a Data Scientist at a Startup Really Looks Like appeared first on Towards Data Science. Yu Dong Go to original source

  • The Generalist: The New All-Around Type of Data Professional?

    The Generalist: The New All-Around Type of Data Professional? Is over-specialization ending and are data generalists on the rise? The post The Generalist: The New All-Around Type of Data Professional? appeared first on Towards Data Science. Loizos Loizou Go to original source

  • Privacy Auditing Synthetic Data Release through Local Likelihood Attacks

    Privacy Auditing Synthetic Data Release through Local Likelihood Attacks arXiv:2508.21146v1 Announce Type: cross Abstract: Auditing the privacy leakage of synthetic data is an important but unresolved problem. Most existing privacy auditing frameworks for synthetic data rely on heuristics and unreasonable assumptions to attack the failure modes of generative models, exhibiting limited capability to describe and…

  • How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker

    How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker From VOC to JSON: Importing pre-annotations made simple The post How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker appeared first on Towards Data Science. Yagmur Gulec Go to original source

  • Graph Coloring for Data Science: A Comprehensive Guide

    Graph Coloring for Data Science: A Comprehensive Guide From theoretical puzzles to practical applications The post Graph Coloring for Data Science: A Comprehensive Guide appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data

    Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data arXiv:2508.11693v1 Announce Type: cross Abstract: Track Circuits (TC) are the main signalling devices used to detect the presence of a train on a rail track. It has been used since the 19th century and nowadays there are many types depending on the…

  • Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models

    Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models arXiv:2508.19249v1 Announce Type: cross Abstract: We present a new efficient hybrid parameter estimation method based on the idea, that if nonlinear dynamic models are stated in terms of a system of equations that is linear in terms of the parameters, then regularized ordinary least squares can…

  • Plato’s Cave and the Shadows of Data

    Plato’s Cave and the Shadows of Data On truth, illusion, and the limits of what data can reveal The post Plato’s Cave and the Shadows of Data appeared first on Towards Data Science. Pol Marin Go to original source

  • Using Google’s LangExtract and Gemma for Structured Data Extraction

    Using Google’s LangExtract and Gemma for Structured Data Extraction Extracting structured information effectively and accurately from long unstructured text with LangExtract and LLMs The post Using Google’s LangExtract and Gemma for Structured Data Extraction appeared first on Towards Data Science. Kenneth Leung Go to original source

  • Can synthetic data reproduce real-world findings in epidemiology? A replication study using tree-based generative AI

    Can synthetic data reproduce real-world findings in epidemiology? A replication study using tree-based generative AI arXiv:2508.14936v1 Announce Type: cross Abstract: Generative artificial intelligence for synthetic data generation holds substantial potential to address practical challenges in epidemiology. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies…

  • My Most Valuable Lesson as an Aspiring Data Analyst

    My Most Valuable Lesson as an Aspiring Data Analyst What my internship taught me about the power of collaboration in data analysis. The post My Most Valuable Lesson as an Aspiring Data Analyst appeared first on Towards Data Science. Benjamin Nweke Go to original source

  • Smooth Flow Matching

    Smooth Flow Matching arXiv:2508.13831v1 Announce Type: new Abstract: Functional data, i.e., smooth random functions observed over a continuous domain, are increasingly available in areas such as biomedical research, health informatics, and epidemiology. However, effective statistical analysis for functional data is often hindered by challenges such as privacy constraints, sparse and irregular sampling, infinite dimensionality, and…

  • Advanced Prompt Engineering for Data Science Projects

    Advanced Prompt Engineering for Data Science Projects Part 2: Prompt Engineering for Features, Modeling, and Evaluation The post Advanced Prompt Engineering for Data Science Projects appeared first on Towards Data Science. Sara Nobrega Go to original source

  • Robust Data Fusion via Subsampling

    Robust Data Fusion via Subsampling arXiv:2508.12048v1 Announce Type: new Abstract: Data fusion and transfer learning are rapidly growing fields that enhance model performance for a target population by leveraging other related data sources or tasks. The challenges lie in the various potential heterogeneities between the target and external data, as well as various practical concerns…

  • Modular Arithmetic in Data Science

    Modular Arithmetic in Data Science Modular arithmetic is a mathematical system where numbers cycle back to the beginning after reaching a value called the modulus. The system is often referred to as “clock arithmetic” due to its similarity to how analog 12-hour clocks represent time. This article provides a conceptual overview of modular arithmetic and…

  • ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization

    ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization arXiv:2508.11551v1 Announce Type: new Abstract: Determining the optimal data mixture for large language model training remains a challenging problem with an outsized impact on performance. In practice, language model developers continue to rely on heuristic exploration since no learning-based approach has emerged as a…

  • Nonparametric learning of stochastic differential equations from sparse and noisy data

    Nonparametric learning of stochastic differential equations from sparse and noisy data arXiv:2508.11597v1 Announce Type: new Abstract: The paper proposes a systematic framework for building data-driven stochastic differential equation (SDE) models from sparse, noisy observations. Unlike traditional parametric approaches, which assume a known functional form for the drift, our goal here is to learn the entire…

  • R-Zero : Self-Evolving Reasoning LLM from Zero Data

    R-Zero : Self-Evolving Reasoning LLM from Zero Data R-Zero by Tencent introduces a concept to train LLMs without any labelled data and aims towards self-improving AI without human intervention. It works on the similar principle of GANs i.e. involving a Challenger and Solver where one generates questions and other Solves them. Paper : https://arxiv.org/abs/2508.05004?ref=mackenziemorehead.com Video…

  • How different is “Senior Data Analyst” from “Data Scientist”?

    How different is “Senior Data Analyst” from “Data Scientist”? I often see Senior DA roles that seem focused on using R/Python for analysis (vs. Excel and Power BI), but don’t have any insight into the day-to-day of theese roles. At the senior level, how different is Data Analyst from Data Scientist? submitted by /u/empirical-sadboy [link]…

  • Data Mesh Diaries: Realities from Early Adopters

    Data Mesh Diaries: Realities from Early Adopters Early-adopter realities gathered from real data mesh implementations The post Data Mesh Diaries: Realities from Early Adopters appeared first on Towards Data Science. Corné POTGIETER Go to original source

  • Projection-based multifidelity linear regression for data-scarce applications

    Projection-based multifidelity linear regression for data-scarce applications arXiv:2508.08517v1 Announce Type: new Abstract: Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifidelity methods for multiple-input multiple-output linear regression targeting data-limited applications with high-dimensional outputs. Multifidelity methods integrate many inexpensive low-fidelity model evaluations…

  • Reducing Time to Value for Data Science Projects: Part 4

    Reducing Time to Value for Data Science Projects: Part 4 Embrace your inner software developer The post Reducing Time to Value for Data Science Projects: Part 4 appeared first on Towards Data Science. Kristopher McGlinchey Go to original source

  • Federated Online Learning for Heterogeneous Multisource Streaming Data

    Federated Online Learning for Heterogeneous Multisource Streaming Data arXiv:2508.06652v1 Announce Type: new Abstract: Federated learning has emerged as an essential paradigm for distributed multi-source data analysis under privacy concerns. Most existing federated learning methods focus on the “static” datasets. However, in many real-world applications, data arrive continuously over time, forming streaming datasets. This introduces additional…

  • Estimating from No Data: Deriving a Continuous Score from Categories

    Estimating from No Data: Deriving a Continuous Score from Categories A walk-through of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training. We use it to predict the severity of an infection on a scale based on information on just rough outcomes in previous cases.…

  • Business focused data science

    Business focused data science As a microbiology researcher, I’m far away from the business world. I do more -omics and growth curves and molecular techniques, but I want to move away from biology. I believe the bridge that can help me do that is data. I have got experience with R and excel. I’m looking…

  • How I Won the “Mostly AI” Synthetic Data Challenge

    How I Won the “Mostly AI” Synthetic Data Challenge A deep dive into how post-processing can supercharge synthetic data generation The post How I Won the “Mostly AI” Synthetic Data Challenge appeared first on Towards Data Science. Daniel Gärber Go to original source

  • Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3)

    Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3) Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3) appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source

  • Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis

    Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis arXiv:2508.01341v1 Announce Type: new Abstract: Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-resolution wealth maps that can…

  • From Data Scientist IC to Manager: One Year In

    From Data Scientist IC to Manager: One Year In Three pillars that shaped my first year in data science management - prioritization, empowerment, and recognition The post From Data Scientist IC to Manager: One Year In appeared first on Towards Data Science. Yu Dong Go to original source

  • AdapDISCOM: An Adaptive Sparse Regression Method for High-Dimensional Multimodal Data With Block-Wise Missingness and Measurement Errors

    AdapDISCOM: An Adaptive Sparse Regression Method for High-Dimensional Multimodal Data With Block-Wise Missingness and Measurement Errors arXiv:2508.00120v1 Announce Type: cross Abstract: Multimodal high-dimensional data are increasingly prevalent in biomedical research, yet they are often compromised by block-wise missingness and measurement errors, posing significant challenges for statistical inference and prediction. We propose AdapDISCOM, a novel adaptive…

  • Is there a term for internal processing vs data that needs to be stakeholding/customer facing?

    Is there a term for internal processing vs data that needs to be stakeholding/customer facing? For example I had my physical credit card stolen. I was trying to get information from the CC company about when the card was used so that the local PD could check security cameras. (We thought it was particular person…

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

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