Springer Mathematical Problems in Data Science: Theoretical and Practical Methods

Math for Data Science

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Mathematical Introduction to Data Science

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An Introduction to Statistical Data Science: Theory and Models

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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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Data Science: Theory, Algorithms, and Applications (Transactions on Computer Systems and Networks)

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Elsevier Mathematical Methods in Data Science - Book

Elsevier

Statistics for Data Science and Policy Analysis

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

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Mathematical Methods for Engineering and Science

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

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Advanced Modeling and Data Challenges

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Data Science and SDGs: Challenges, Opportunities and Realities

Introductory Statistics for Data Analysis

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An Excursion Through Partial Differential Equations (Problem Books in Mathematics)

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Data Science and Internet of Things: Research and Applications at the Intersection of DS and IoT

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Big Data Analytics: Theory, Techniques, Platforms, and Applications (SpringerBriefs in Applied Sciences and Technology)

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Frame Theory in Data Science (Advances in Science, Technology & Innovation)

Variational Methods in Mathematics, Science and Engineering

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Social Big Data Analytics: Practices, Techniques, and Applications

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Regression Techniques for Data Analysis

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Smart Computational Strategies: Theoretical and Practical Aspects

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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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Financial Data Analytics: Theory and Application (Contributions to Finance and Accounting)

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