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KDnuggets™ is a leading site on Business Analytics, Big Data, Data Mining, Data Science, and Machine Learning and is edited by Gregory Piatetsky-Shapiro (email to editor1 at kdnuggets) and Matthew Mayo (email to mattmayo at kdnuggets). KDnuggets received over 50 awards/mentions as a leading publication, including In 20 Data Industry Experts You Need to Follow on Twitter, Churchill Frank, Oct 2017. In Top 10 Most Influential Brands on Big Data, Onalytica, May 2017. No. 3 in Top 75 Data Science Blogs by Feedspot, Jan 2017. No. 1 in Agilience Top Authorities in Machine Learning, Nov 2016. No. 1 in Agilience Top Authorities for Data Mining, No. 2 for Data Science, Nov 2016. No. 3 in AI Intelligence & Machine Learning: Top 100 Influencers and Brands, Onalytica, Mar 2016. No. 4 in Big Data 2016: Top 100 Influencers, Onalytica, Feb 2016. In InformationWeek Twitter Top 10 Data Science, Analytics, And BI Feeds among IBM Big Data & Analytics Heroes In top 30 People in Big Data and Analytics, Innovation Enterprise profiled in INFORMS: KDnuggets Serves Analytics and Big Data Fields in Information Management 7 Business Analytics Gurus on Twitter voted the Best Big Data Tweeter by Big Data Republic featured on Forbes, among Top Influencers in Big Data Source

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Scope Trade/B2B, Consumer
Language English
Country United States of America
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Running OpenClaw with Ollama

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By Shittu Olumide
| KDnuggets Verified
This article covers the full path from zero to a running private research assistant on Telegram, including configuring the context length correctly, connecting the channel, enabling web search, and deploying it headlessly in Docker. You have successfully set up Ollama, pulled a capable model, run a few queries in the terminal, and it worked. The responses were sharp. The latency was real. The whole thing ran on your own hardware with no API key and no cloud.
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7 Steps to Automating Descriptive Statistics with Python

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By Kanwal Mehreen
| KDnuggets Verified
Stop writing mean() and std() for every column. Learn how to automate descriptive statistics in Python and generate publication-ready summary tables in just a few steps. Every analysis starts the same way: you load a dataset and try to figure out what's actually in it. How many rows? Which columns are numeric? How much is missing? Is anything wildly skewed?
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How to Clean Messy CSV Files with Python: A Beginner’s Guide

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By Abid Ali Awan Verified
| KDnuggets Verified
Learn how to clean CSV files with pandas by handling missing values, duplicate rows, messy text, wrong data types, mixed date formats, invalid emails, and currency values. When you are just starting out with data analysis, one of the first things you learn is how to clean a dataset. It sounds basic, but it is one of the most important skills you will use again and again.
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SQL vs Pandas vs AI Agents: Which Solves Analytics Problems Best?

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By Nate Rosidi
| KDnuggets Verified
Same three analytics problems, three tools, eight dimensions, measured with real execution times and real agent prompts. We gave the same three interview questions from StrataScratch to SQL, Pandas, and a Claude agent. Every piece of code executed against the same dataset, and every timing number is a median over 500 runs. The agent's answers are exactly what Claude generated in response to a documented prompt, instead of a hypothetical example of what an agent might produce.
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Zero-Shot Local Document Parsing with Gemma 4: Treating PDFs as Images

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By Shittu Olumide, instantiate PDFRenderer, no Ghostscript
| KDnuggets Verified
Treating PDFs as images and feeding those images to Gemma 4 dissolves the scanned-versus-digital distinction that makes every text-extraction pipeline fragile. Fix that. Run on a scanned invoice, and you get nothing. Run it on a multi-column research paper, and you get a stream of text that has lost every spatial relationship the layout encoded. Run it on a filled PDF form, and you get the field labels concatenated with the values in reading order, with no way to tell which belongs to which.
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10 Probability Concepts for Machine Learning Explained Simply

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By Kanwal Mehreen
| KDnuggets Verified
A model is almost never 100% sure of anything. These 10 probability concepts explain how it makes decisions anyway. For a long time I treated probability as the vegetables of machine learning. The boring stuff you choke down before you get to the good part. Later on, I realized that probability is not just a prerequisite for machine learning but it makes much of machine learning work. But nobody tells you up front that a model is almost never sure of anything. Is this image a cat? Probably.
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Data Scientists Are Becoming AI Managers, Not Model Builders

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By Nate Rosidi
| KDnuggets Verified
The role is shifting from building models to managing them. Data scientists at companies running AI in production are spending more time on AI oversight and system supervision than on model construction. Job postings and salary data from 2025 and 2026 confirm it. LinkedIn's 2025 data identified AI literacy and large language model (LLM) proficiency as two of the fastest-growing skills globally. Lightcast found that 51% of AI-related job postings now sit outside traditional IT roles.
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Getting Started with Hugging Face ML Intern: Your First ML Agent

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By Kanwal Mehreen
| KDnuggets Verified
You describe the model. It writes the code, runs the training, and ships the checkpoint. Welcome to ML Intern. Have you ever gotten stuck in that familiar situation where you have a model idea, but the gap between “I read the paper” and “I have a trained checkpoint on the Hub” still eats up an entire weekend? ML Intern is Hugging Face's attempt to shrink that gap. It is an open-source command-line interface (CLI) agent from Hugging Face that lets you describe machine learning tasks in plain English.
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5 Ways Small Language Models Are Powering Next-Gen Agents

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By Shittu Olumide
| KDnuggets Verified
This article looks at five concrete ways SLMs are showing up inside next-generation agents right now, from the research backing them to the tools and numbers worth knowing if you're deciding whether your next agent needs a frontier model at all For the last two years, the assumption in agentic AI was simple: the bigger the model, the better the agent. Bigger context windows, more parameters, sharper reasoning. What's not to like?
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Getting Started with the Claude API in Python

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By Bala Priya C
| KDnuggets Verified
In this article, you'll learn how to use the Claude API in Python, make your first request, and handle responses with the official SDK. You want to add Claude to a Python application. Creating an account and making your first API call is straightforward. The official documentation can get you from zero to a working request in a few minutes. The next questions are usually more practical: • What does the response object contain? • How do you stream responses so users can see output as it's generated?
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KDnuggets Journalists

Awan, Abid Ali
Mayo, Matthew