A large free archive of practitioner-written tutorials and explainers on machine learning, statistics, data engineering and AI, from beginner to advanced.
Why I recommend it: Quality varies by author, but the beginner explainers are among the easiest free routes into data work.
A KDnuggets tutorial by Abid Ali Awan that walks through scraping thousands of pages with the Olostep Batch API in Python, downloading the results as Markdown and checking how many requests completed. Running the code requires an Olostep API key.
A KDnuggets tutorial by Bala Priya C with five beginner Python scripts for automating repetitive tasks, including organizing files, processing images and simplifying everyday workflows, with code and explanations.
A DeepLearning.AI course built with Prior Labs, about 1.5 hours at the intermediate level, on using tabular foundation models such as TabPFN for prediction on spreadsheet-style data and connecting them to data science agents. Enrollment requires a free account.
A Monster career article by Vicki Salemi, published September 30, 2026, on six skilled trades with strong projected job demand, with typical pay, training paths and outlook for each.
RudderStack's free library of explainer articles on data engineering, data infrastructure and machine learning, covering topics such as generalization, deep learning and supplying customer data to LLM applications. RudderStack sells a customer data platform.
A KDnuggets guide by Vinod Chugani that goes round by round through current data science hiring processes, including AI-assisted coding rounds and generative AI evaluation questions, and lists ways candidates can prepare.