6 Technical Skills That Make You a Senior Data ScientistTowards Data Science 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.
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. Read More
How Transformers Think: The Information Flow That Makes Language Models WorkKDnuggets Let’s uncover how transformer models sitting behind LLMs analyze input information like user prompts and how they generate coherent, meaningful, and relevant output text “word by word”.
Let’s uncover how transformer models sitting behind LLMs analyze input information like user prompts and how they generate coherent, meaningful, and relevant output text “word by word”. Read More
Stop Writing Spaghetti if-else Chains: Parsing JSON with Python’s match-caseTowards Data Science Introduction If you work in data science, data engineering, or as as a frontend/backend developer, you deal with JSON. For professionals, its basically only death, taxes, and JSON-parsing that is inevitable. The issue is that parsing JSON is often a serious pain. Whether you are pulling data from a REST API, parsing logs, or reading
The post Stop Writing Spaghetti if-else Chains: Parsing JSON with Python’s match-case appeared first on Towards Data Science.
Introduction If you work in data science, data engineering, or as as a frontend/backend developer, you deal with JSON. For professionals, its basically only death, taxes, and JSON-parsing that is inevitable. The issue is that parsing JSON is often a serious pain. Whether you are pulling data from a REST API, parsing logs, or reading
The post Stop Writing Spaghetti if-else Chains: Parsing JSON with Python’s match-case appeared first on Towards Data Science. Read More
CEOs still betting big on AI: Strategy vs. return on investment in 2026AI News Enterprise leaders are pressing ahead with artificial intelligence, even as some early results remain uneven. Reporting from the Wall Street Journal and Reuters shows that most CEOs expect AI spending to keep rising through 2026, despite difficulty tying those investments to clear, enterprise-wide returns. The tension highlights where many organisations now sit in their AI
The post CEOs still betting big on AI: Strategy vs. return on investment in 2026 appeared first on AI News.
Enterprise leaders are pressing ahead with artificial intelligence, even as some early results remain uneven. Reporting from the Wall Street Journal and Reuters shows that most CEOs expect AI spending to keep rising through 2026, despite difficulty tying those investments to clear, enterprise-wide returns. The tension highlights where many organisations now sit in their AI
The post CEOs still betting big on AI: Strategy vs. return on investment in 2026 appeared first on AI News. Read More
EpiPlanAgent: Agentic Automated Epidemic Response Planningcs.AI updates on arXiv.org arXiv:2512.10313v2 Announce Type: replace
Abstract: Epidemic response planning is essential yet traditionally reliant on labor-intensive manual methods. This study aimed to design and evaluate EpiPlanAgent, an agent-based system using large language models (LLMs) to automate the generation and validation of digital emergency response plans. The multi-agent framework integrated task decomposition, knowledge grounding, and simulation modules. Public health professionals tested the system using real-world outbreak scenarios in a controlled evaluation. Results demonstrated that EpiPlanAgent significantly improved the completeness and guideline alignment of plans while drastically reducing development time compared to manual workflows. Expert evaluation confirmed high consistency between AI-generated and human-authored content. User feedback indicated strong perceived utility. In conclusion, EpiPlanAgent provides an effective, scalable solution for intelligent epidemic response planning, demonstrating the potential of agentic AI to transform public health preparedness.
arXiv:2512.10313v2 Announce Type: replace
Abstract: Epidemic response planning is essential yet traditionally reliant on labor-intensive manual methods. This study aimed to design and evaluate EpiPlanAgent, an agent-based system using large language models (LLMs) to automate the generation and validation of digital emergency response plans. The multi-agent framework integrated task decomposition, knowledge grounding, and simulation modules. Public health professionals tested the system using real-world outbreak scenarios in a controlled evaluation. Results demonstrated that EpiPlanAgent significantly improved the completeness and guideline alignment of plans while drastically reducing development time compared to manual workflows. Expert evaluation confirmed high consistency between AI-generated and human-authored content. User feedback indicated strong perceived utility. In conclusion, EpiPlanAgent provides an effective, scalable solution for intelligent epidemic response planning, demonstrating the potential of agentic AI to transform public health preparedness. Read More
The Skills That Bridge Technical Work and Business ImpactTowards Data Science In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science and AI, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Maria Mouschoutzi. Maria is a Data Analyst and Project Manager with a strong background in Operations Research, Mechanical
The post The Skills That Bridge Technical Work and Business Impact appeared first on Towards Data Science.
In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science and AI, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Maria Mouschoutzi. Maria is a Data Analyst and Project Manager with a strong background in Operations Research, Mechanical
The post The Skills That Bridge Technical Work and Business Impact appeared first on Towards Data Science. Read More
The Machine Learning “Advent Calendar” Day 14: Softmax Regression in ExcelTowards Data Science 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 of parallel scores increases.
Implemented in Excel, the model becomes transparent: you can see the scores, the probabilities, and how the coefficients evolve over time.
The post The Machine Learning “Advent Calendar” Day 14: Softmax Regression in Excel appeared first on Towards Data Science.
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 of parallel scores increases.
Implemented in Excel, the model becomes transparent: you can see the scores, the probabilities, and how the coefficients evolve over time.
The post The Machine Learning “Advent Calendar” Day 14: Softmax Regression in Excel appeared first on Towards Data Science. Read More
Lessons Learned from Upgrading to LangChain 1.0 in ProductionTowards Data Science 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.
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. Read More
Deep-learning model predicts how fruit flies form, cell by cellMIT News – Machine learning The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.
The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease. Read More
Meta-Statistical Learning: Supervised Learning of Statistical Estimatorscs.AI updates on arXiv.org arXiv:2502.12088v3 Announce Type: replace-cross
Abstract: Statistical inference, a central tool of science, revolves around the study and the usage of statistical estimators: functions that map finite samples to predictions about unknown distribution parameters. In the frequentist framework, estimators are evaluated based on properties such as bias, variance (for parameter estimation), accuracy, power, and calibration (for hypothesis testing). However, crafting estimators with desirable properties is often analytically challenging, and sometimes impossible, e.g., there exists no universally unbiased estimator for the standard deviation. In this work, we introduce meta-statistical learning, an amortized learning framework that recasts estimator design as an optimization problem via supervised learning. This takes a fully empirical approach to discovering statistical estimators; entire datasets are input to permutation-invariant neural networks, such as Set Transformers, trained to predict the target statistical property. The trained model is the estimator, and can be analyzed through the classical frequentist lens. We demonstrate the approach on two tasks: learning a normality test (classification) and estimating mutual information (regression), achieving strong results even with small models. Looking ahead, this paradigm opens a path to automate the discovery of generalizable and flexible statistical estimators.
arXiv:2502.12088v3 Announce Type: replace-cross
Abstract: Statistical inference, a central tool of science, revolves around the study and the usage of statistical estimators: functions that map finite samples to predictions about unknown distribution parameters. In the frequentist framework, estimators are evaluated based on properties such as bias, variance (for parameter estimation), accuracy, power, and calibration (for hypothesis testing). However, crafting estimators with desirable properties is often analytically challenging, and sometimes impossible, e.g., there exists no universally unbiased estimator for the standard deviation. In this work, we introduce meta-statistical learning, an amortized learning framework that recasts estimator design as an optimization problem via supervised learning. This takes a fully empirical approach to discovering statistical estimators; entire datasets are input to permutation-invariant neural networks, such as Set Transformers, trained to predict the target statistical property. The trained model is the estimator, and can be analyzed through the classical frequentist lens. We demonstrate the approach on two tasks: learning a normality test (classification) and estimating mutual information (regression), achieving strong results even with small models. Looking ahead, this paradigm opens a path to automate the discovery of generalizable and flexible statistical estimators. Read More