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Top rated mathematical and statistical software books

Here are some top-rated mathematical and statistical software books:

Mathematical Software:

  1. "Numerical Recipes in C: The Art of Scientific Computing" by William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery - a classic book on numerical methods and algorithms.
  2. "Mathematica: A System for Doing Mathematics by Computer" by Stephen Wolfram - a comprehensive guide to Mathematica, a popular mathematical software.
  3. "Matlab: An Introduction with Applications" by Amos Gilat - a beginner's guide to Matlab, a widely used mathematical software.
  4. "Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython" by Wes McKinney - a book on using Python for data analysis, with a focus on Pandas, NumPy, and IPython.
  5. "R for Data Science: Import, Tidy, Transform, Visualize, and Model Data" by Hadley Wickham and Garrett Grolemund - a comprehensive guide to using R for data science.

Statistical Software:

  1. "SAS for Data Analysis: Intermediate Statistical Methods" by Michael Friendly - a book on using SAS for intermediate-level statistical analysis.
  2. "SPSS for Introductory and Intermediate Statistics" by George A. Morgan, Nancy L. Leech, Gene W. Gloeckner, and Karen C. Barrett - a book on using SPSS for introductory and intermediate-level statistical analysis.
  3. "R: A Language and Environment for Statistical Computing" by R Core Team - the official guide to R, a popular statistical software.
  4. "Python for Predictive Analytics: Principles and Practice of Machine Learning and Predictive Modeling with Python" by Wes McKinney and Max Asay - a book on using Python for predictive analytics and machine learning.
  5. "Statistics with Stata: Updated for Version 12" by Lawrence C. Hamilton - a book on using Stata, a popular statistical software.

Machine Learning and Data Science:

  1. "Pattern Recognition and Machine Learning" by Christopher M. Bishop - a comprehensive guide to machine learning and pattern recognition.
  2. "Data Mining: Practical Machine Learning Tools and Techniques" by Ian H. Witten, Eibe Frank, and Mark A. Hall - a book on data mining and machine learning with practical examples.
  3. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - a comprehensive guide to deep learning.
  4. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurélien Géron - a practical guide to machine learning with Python.
  5. "Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking" by Foster Provost and Tom Fawcett - a book on data science and its applications in business.

These books are highly rated and widely used in the field, but it's worth noting that there are many other excellent books available, and the best book for you will depend on your specific needs and goals.