Top Mathematics discussions
NishMath - #Prediction
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Amir Najmi@unofficialgoogledatascience.com
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Data scientists and statisticians are continuously exploring methods to refine data analysis and modeling. A recent blog post from Google details a project focused on quantifying the statistical skills necessary for data scientists within their organization, aiming to clarify job descriptions and address ambiguities in assessing practical data science abilities. The authors, David Mease and Amir Najmi, leveraged their extensive experience conducting over 600 interviews at Google to identify crucial statistical expertise required for the "Data Scientist - Research" role.
Statistical testing remains a cornerstone of data analysis, guiding analysts in transforming raw numbers into actionable insights. One must also keep in mind bias-variance tradeoff and how to choose the right statistical test to ensure the validity of analyses. These tools are critical for both traditional statistical roles and the evolving field of AI/ML, where responsible practices are paramount, as highlighted in discussions about the relevance of statistical controversies to ethical AI/ML development at an AI ethics conference on March 8.
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@phys.org
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A new mathematical model developed by the University of Rovira i Virgili's SeesLab research group, along with researchers from Northeastern University and the University of Pennsylvania, has made it possible to predict human mobility between cities with high precision. The model offers a simpler and more efficient way than current systems and is a valuable tool for understanding how people move in different contexts, which is crucial for transport planning, migration studies, and epidemiology. The research was published in the journal *Nature Communications*.
The model builds on traditional "gravitational models," which estimate mobility based on population size and distance between cities. While these models are simple, they lack accuracy. Modern approaches leverage artificial intelligence and machine learning to incorporate many variables besides origin and destination, such as the density of restaurants and schools, and the socio-demographic characteristics of the population. The COVID-19 pandemic highlighted the importance of predicting mobility for understanding the spread and evolution of viruses.
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