Data Science and Predictive Analytics

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https://en.wikipedia.org/wiki/Data_Science_and_Predictive_Analytics
Data Science and Predictive Analytics: Biomedical and Health Applications using R
Data Science and Predictive Analytics (book cover).png
Author Ivo D. Dinov
CountrySwitzerland
LanguageEnglish
Subject Computer science, Data science, artificial intelligence
Publisher Springer
Publication date
2018
Media typePrint (hardcover)
ISBN 978-3-319-72346-4

The textbook Data Science and Predictive Analytics: Biomedical and Health Applications using R, authored by Ivo D. Dinov, was published in August 2018 by Springer. [1]

This textbook covers some of the mathematical foundations, computational techniques, and artificial intelligence approaches used in data science research and applications. [2]

Using the statistical computing platform R and a broad range of biomedical case-studies, the 23 chapters of the book provide explicit examples of importing, exporting, processing, modeling, visualizing, and interpreting large, multivariate, incomplete, heterogeneous, longitudinal, and incomplete datasets ( big data). [3]

Structure

The Data Science and Predictive Analytics textbook is divided into the following 23 chapters, each progressively building on the previous content.

  1. Motivation
  2. Foundations of R
  3. Managing Data in R
  4. Data Visualization
  5. Linear Algebra & Matrix Computing
  6. Dimensionality Reduction
  7. Lazy Learning: Classification Using Nearest Neighbors
  8. Probabilistic Learning: Classification Using Naive Bayes
  9. Decision Tree Divide and Conquer Classification
  10. Forecasting Numeric Data Using Regression Models
  11. Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines
  12. Apriori Association Rules Learning
  13. k-Means Clustering
  14. Model Performance Assessment
  15. Improving Model Performance
  16. Specialized Machine Learning Topics
  17. Variable/Feature Selection
  18. Regularized Linear Modeling and Controlled Variable Selection
  19. Big Longitudinal Data Analysis
  20. Natural Language Processing/Text Mining
  21. Prediction and Internal Statistical Cross Validation
  22. Function Optimization
  23. Deep Learning, Neural Networks

Reception

The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer reviewed in the International Statistical Institute’s ISI Review Journal [2] and the Journal of the American Library Association. [3] Many scholarly publications reference the DSPA textbook. [4] [5]

As of January 17, 2021, the electronic version of the book ( ISBN  978-3-319-72347-1) is freely available on SpringerLink [6] and has been downloaded over 6 million times. The textbook is globally available in print and electronic formats in many college and university libraries [7] and has been used for data science, computational statistics, and analytics classes at various institutions. [8]

References

External links