Datanalytics101 · Medical AI Insights
Free research training for AI in Medicine
You don’t need a conference ticket to benefit from it — a step-by-step system for extracting tutorials, papers, and research directions from any top-tier venue. Expanded 2025 edition — originally published May 2019 Following conferences strategically — even without attending — is one of the highest-leverage habits a researcher can build. Here’s a systematic approach … Read more
Image by StockSnap from Pixabay
Missed the deadline? Your work isn’t wasted — a practical guide to preprint servers, journals, and workshops as viable publishing routes for AI and ML research. Expanded 2025 edition — originally published August 2022 The quick answer is yes. The full answer involves understanding preprints, journals, transactions, and when each is the right choice for … Read more
The essential textbooks for ML, deep learning, NLP, and medical AI — with honest notes on who each book is for and where to find free legal copies. Photo credit: danielfoster437 Library Books via photopin (license) Updated 2025 — originally published December 2018 A curated reading list of the essential textbooks for ML, NLP, deep … Read more
Updated 2025 The best way to learn how to present research is to study real presentations. Here are my slides from over a decade of conference talks — with honest commentary on what worked, what I’d change, and what you can take directly as a template. Most advice on research presentations is abstract. “Tell a … Read more
From zero to your first working analysis — install R, set up RStudio, and learn the core tools every data scientist needs. Last Updated 2026 — originally published July 2018 R is still one of the most powerful tools in a data scientist’s toolkit — especially for statistical modeling, EDA, and clinical research. This guide … Read more
From raw data to publication-quality visualizations — master EDA with dplyr and ggplot2 before you build your first model. Updated 2026 edition — originally published August 2018 Exploratory Data Analysis (EDA) is the most important step before any machine learning model — and R makes it fast, visual, and intuitive. This tutorial covers everything from … Read more
Using our paper on Parkinson’s Disease subtyping as a case study, I share the essential steps for navigating the complex world of medical research. Featured Image by Erik Mclean on Unsplash Given the wide accessibility to medical imaging and NLP datasets and benchmarking efforts of LLMs, clinical research with patient data still has higher barriers … Read more
A step-by-step guide with a real case study for busy industry professionals to write quality AI papers without burnout. If you’re reading this, chances are you’re an AI professional juggling a demanding full-time job and wondering how to get your research out there without losing your mind. Publishing a paper at a top AI conference … Read more
From raw text to model-ready features — a hands-on guide to the NLP libraries every data scientist and researcher needs to know. A practical, code-first guide to natural language processing in Python — from raw text to model-ready features, using the libraries every NLP researcher needs to know. NLP in 2025 is dominated by large … Read more