Springer Advanced Modeling and Data Challenges

Math for Data Science

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Applied Data Analysis and Modeling for Energy Engineers and Scientists

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Mathematical Introduction to Data Science

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Mathematical Problems in Data Science: Theoretical and Practical Methods

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Advances and Challenges in Computational Mechanics

An Introduction to Statistical Data Science: Theory and Models

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Data Visualization: Trends and Challenges Toward Multidisciplinary Perception

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Recent Trends and Future Challenges in Learning from Data (Studies in Classification, Data Analysis, and Knowledge Organization)

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Data Science and SDGs: Challenges, Opportunities and Realities

Data Analysis of Medical Studies: Reading and Reporting

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Statistical Modeling and Computation (Springer Texts in Statistics)

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Computing for Data Analysis: Theory and Practices (Data-Intensive Research)

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Computing for Data Analysis: Theory and Practices (Data-Intensive Research)

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Springer Multi-dimensional Additive Manufacturing Book

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Multi-faceted Deep Learning: Models and Data

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Robust Representation for Data Analytics: Models and Applications (Advanced Information and Knowledge Processing)

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Springer - Computational Methods in Engineering Book

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Modularity Methodology

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Springer Advanced Filter Structure Handbook - Data Analytics

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Springer - Assessing COVID-19 and Pandemics via Modelling

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Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective

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Machine Learning for Advanced Functional Materials

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Introductory Statistics for Data Analysis

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Statistical Models and Methods for Data Science (Studies in Classification, Data Analysis, and Knowledge Organization)

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