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Ryan BurninTowards Data ScienceHow to Efficiently Approximate a Function of One or More VariablesUse sparse grids and Chebyshev interpolants to build accurate approximations to multivariable functions.Jun 282Jun 282
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Khouloud El AlamiinTowards Data ScienceMaster This Data Science Skill and You Will Land a Job In Big Tech— Part IFundamentals of experimentation I learned from working with the best in the tech industry1d ago2
Yuki ShizuyainIntuitionStatistics: Time Series Analysis — Compilation of the fundamental conceptsGentle introduction of time series analysis basics with visualization and detailed mathematical derivation3d ago
Samuele MazzantiinTowards Data ScienceForget Statistical Tests: A/B Testing Is All About SimulationsHow simulations outperform traditional stats in that they are easier to understand, more flexible, and economically meaningful4d ago4
Ryan BurninTowards Data ScienceHow to Efficiently Approximate a Function of One or More VariablesUse sparse grids and Chebyshev interpolants to build accurate approximations to multivariable functions.Jun 282
Yuki ShizuyainThe Quantastic JournalMathematical understanding of Gaussian ProcessDetailed explanation of mathematical background of Gaussian process with necessary concepts and visualizationJun 242
W Brett KennedyinTowards Data ScienceCounts Outlier Detector: Interpretable Outlier DetectionAn interpretable outlier detector based on multi-dimensional histograms.Jun 191
Samuele MazzantiinTowards Data ScienceHypothesis Testing Explained (How I Wish It Was Explained to Me)Most resources focus on things like Confidence and Power. But they don’t really matter: here is what you should care aboutMay 134
Pritul PatelThe True History of P-values and Hypothesis Testing: What Every A/B Testing Practitioner Should…Understanding this crucial piece of history will remove the confusion from your experimentation results2h ago
