Discovering Gems: Assorted Data Science Links for Every Enthusiast

The world of data science is ever-evolving, with new tools, research, and insights emerging daily. For enthusiasts and professionals alike, staying updated is essential—but it can also feel overwhelming. To help you navigate this dynamic landscape, we’ve curated a selection of assorted links covering a range of topics in data science. Whether you're diving into advanced machine learning techniques, exploring best practices for data visualization, or simply seeking inspiration from successful projects, these links are sure to spark your curiosity and enhance your knowledge.

1. Learn the Basics of Well-Structured Data. Follow these three data-structure tips to recognize the best, and smooth out the rest (Tableau).
2. Introducing Wake Vision: A High-Quality, Large-Scale Dataset for TinyML Computer Vision Applications (TensorFlow Blog).
3. A Tutorial on Teaching Data Analytics with Generative AI. This tutorial addresses the challenge of incorporating large language models, such as ChatGPT, in a data analytics class. It details several new in-class and out-of-class teaching techniques enabled by artificial intelligence (AI). Here are three examples: Instructors can parallelize instruction by having students interact with different custom-made GPTs to learn different parts of an analysis and then teach each other what they learned from their GPTs. Instructors can turn problem sets into AI tutoring sessions: a custom-made GPT guides a student through the problems and the student uploads the chatlog for their homework submission. Instructors can assign different labs to each section of a class and have each section create AI assistants to help the other sections work through their labs. This tutorial advocates the natural-language programming (NLP) paradigm, in which students articulate desired data transformations with a spoken language, such as English, and then use AI to generate the corresponding computer code. Students can wrangle data more effectively with NLP than with Excel (Robert L. Bray).
4. Using ChatGPT to Generate NLP-Driven Investment Strategies (Enterprising Investor).
5. Probabilistic weather forecasting with machine learning. Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)1, which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range Weather Forecasts4. GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude–longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently (Nature).
6. Finally, Grid Computing Simplified with the LFAI CLAIMED framework (IBM Data Science in Practice).

The journey through the vast field of data science is one of constant learning and discovery. We hope this collection of links serves as a valuable resource, whether you’re honing your skills, tackling new challenges, or staying ahead of industry trends. If you found these links helpful, feel free to share them with your network and let us know which ones stood out to you. Together, let’s continue uncovering the potential of data science to transform industries, solve problems, and inspire innovation. Happy exploring!

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