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Build ETL Pipelines for Data Science Workflows in About 30 Lines of Python

KDnuggets

Start here with a simple Python pipeline that covers the essentials. Well grab data from a CSV file (like youd download from an e-commerce platform), clean it up, and store it in a proper database for analysis. In this article, Ill walk you through creating a pipeline that processes e-commerce transactions.

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10 Free Online Courses to Master Python in 2025

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Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 10 Free Online Courses to Master Python in 2025 How can you master Python for free?

Python 242
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How I Automated My Machine Learning Workflow with Just 10 Lines of Python

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Sign in Sign out Contributor Portal Latest Editor’s Picks Deep Dives Contribute Newsletter Toggle Mobile Navigation LinkedIn X Toggle Search Search Data Science How I Automated My Machine Learning Workflow with Just 10 Lines of Python Use LazyPredict and PyCaret to skip the grunt work and jump straight to performance.

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Run the Full DeepSeek-R1-0528 Model Locally

KDnuggets

Download and configure the 1.78-bit Install it on an Ubuntu distribution using the following commands: apt-get update apt-get install pciutils -y curl -fsSL [link] | sh Step 2: Download and Run the Model Run the 1.78-bit In this tutorial, we will: Set up Ollama and Open Web UI to run the DeepSeek-R1-0528 model locally.

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Apache Airflow® Crash Course: From 0 to Running your Pipeline in the Cloud

With over 30 million monthly downloads, Apache Airflow is the tool of choice for programmatically authoring, scheduling, and monitoring data pipelines. Airflow enables you to define workflows as Python code, allowing for dynamic and scalable pipelines suitable to any use case from ETL/ELT to running ML/AI operations in production.

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Automate Data Quality Reports with n8n: From CSV to Professional Analysis

KDnuggets

No Python environment setup, no manual coding, no switching between tools. Unlike writing standalone Python scripts, n8n workflows are visual, reusable, and easy to modify. This routine gets tedious when youre evaluating multiple datasets daily.

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A Gentle Introduction to Principal Component Analysis (PCA) in Python

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By Iván Palomares Carrascosa , KDnuggets Technical Content Specialist on July 4, 2025 in Python Image by Author | Ideogram Principal component analysis (PCA) is one of the most popular techniques for reducing the dimensionality of high-dimensional data.

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