Hamza SAOULI, Ph.D.

Another of the favorite tutorials i made, Network Meta-Analysis in R, part 2. In this edition : 1. Combining the analysis of Direct estimates with the Network Effects (Indirect and Direct) 2. Creating the publication ready Network graph for Direct evidence 3. Creating the Network effects forest plots 4. Combining it all togheader 5. Package used 'netmeta' using RStudio by Posit PBC If you think additional tutorials should be made by me in this field, do let me know. The link to this full tutorial in the comments...

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🚀Great news for anyone interested in deep learning! 🎉 📘 🐍 The 3𝐫𝐝 𝐞𝐝𝐢𝐭𝐢𝐨𝐧 𝐨𝐟 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 by François Chollet and Matthew Watson is coming soon — and for the first time, a 𝐅𝐑𝐄𝐄 online version will be available. This new edition brings updated coverage of Keras, TensorFlow, generative AI models, PyTorch, and JAX, with hands-on Python examples. The book is available to pre-order: 📘 Publisher (September 22, 2025): https://lnkd.in/dHaA4jBy 📘 Amazon (September 30, 2025): https://lnkd.in/d9Jb2uzm A 𝐅𝐑𝐄𝐄 online version of the book is available here: https://lnkd.in/dq5bQ3-h Thank you to the authors for providing a 𝐅𝐑𝐄𝐄 online version of the book! 🙏 #books #newbooks #freebooks #python #deeplearning #ai #datascience

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The OLTP vs. OLAP split made sense when applications and analytics were separate stacks. But today, the real divide is operational vs. non-operational data: data needed to serve the app vs. data optimized for training, analysis, or governance. That’s Postgres + the lakehouse. And increasingly, it’s structured as an operational medallion architecture - bronze to silver to gold - across these layers. Wrote more on the Timescale blog today: https://lnkd.in/esxRn9UA Interested to hear where you agree or disagree.

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A lovely piece on Towards Data Science by Victor Murcia analyzing a classic NYC data set in 𝐒𝐪𝐮𝐢𝐫𝐫𝐞𝐥𝐌𝐋: 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 𝐒𝐪𝐮𝐢𝐫𝐫𝐞𝐥 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐢𝐧 𝐍𝐘𝐂’𝐬 𝐂𝐞𝐧𝐭𝐫𝐚𝐥 𝐏𝐚𝐫𝐤. The tutorial comes with the description of a streamlit app to run an actual squirrel forecasting app as well! More: https://lnkd.in/dxtZm62R #365Papers #Day39

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Don't be fooled by the name; the Boring Semantic Layer by Julien Hurault and Xorq Labs is one of the most exciting projects to watch! I've been toying with the idea of implementing a semantic layer on top of Kedro's Data Catalog. When I led data engineering teams at QuantumBlack, AI by McKinsey, one of the first activities we performed at any client was connecting and exploring their data. However, ad-hoc analyses didn't directly leverage the data models we'd painstakingly built. The Boring Semantic Layer provides built-in querying and charting capabilities on top of those data models and even exposes an MCP server for AI-driven answers! Everything is still very much a work in progress (I'm building on top of a bleeding-edge branch of Boring Semantic Layer without asking 🤫). One of the things I still don't know is whether people actually want to access (or even augment) their semantic layer from within their data pipelines or if it's mostly for BI/reporting. To what extent does a semantic layer replace feature engineering?

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Data science has evolved tremendously in recent years, largely due to its main application: artificial intelligence. At ARPM, we teach it through a unique symmetry: 🔹 Mean-Covariance Framework — randomness modeled by mean & covariance, affine transformations, and uncorrelation as structure. 🔹 Probabilistic Framework — randomness modeled by full distributions, non-linear transformations, and independence as structure. The symmetry is simple: every advanced probabilistic (AI/ML) method is a direct generalization of a mean-covariance one. Recognizing this makes learning both intuitive and powerful. That’s why ARPM Lab structures Data Science into four pillars: - Mathematical Statistics for Finance - Linear Mean-Covariance Statistics - Probabilistic Machine Learning - Time Series & Sequential Decisions 📍 All summarized in the Data Science Map below. 👉 https://www.arpm.co/

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Have you built your first ML model to predict sales, churns… excellent. 👏what next ? Here’s something I always tell my students: The real game starts after the model is built. The most important part students often miss is thinking end-to-end. It's not just about accuracy — it’s about impact. Ask yourself: -- How would you deploy your model? -- How the data is fed to the model ? -- What technologies would you use — and why? (Think cost, latency, scalability) -- What’s your cadence for data refresh or model retraining? -- How are you tracking performance in production? -- Do you have a monitoring plan for drift or anomalies? If you’ve actually thought about (and built!) a solution that answers these and added to your resume, you’re already ahead of the pack — and much more likely to be considered seriously for a job. ✨ Data science isn't just about predictions — it's about productizing intelligence. #MLOps #Studentlearning #datascience

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