Data Science in Theory and Practice
eBook - ePub

Data Science in Theory and Practice

Techniques for Big Data Analytics and Complex Data Sets

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Data Science in Theory and Practice

Techniques for Big Data Analytics and Complex Data Sets

About this book

DATA SCIENCE IN THEORY AND PRACTICE

EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE

Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling.

The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language.

Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like:

  • Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis
  • A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity
  • Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages
  • An exploration of algorithms, including how to write one and how to perform an asymptotic analysis
  • A comprehensive discussion of several techniques for analyzing and predicting complex data sets

Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

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Yes, you can access Data Science in Theory and Practice by Maria Cristina Mariani,Osei Kofi Tweneboah,Maria Pia Beccar-Varela in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2021
Print ISBN
9781119674689
eBook ISBN
9781119674733

Table of contents

  1. Cover
  2. Table of Contents
  3. Title Page
  4. Copyright
  5. List of Figures
  6. List of Tables
  7. Preface
  8. 1 Background of Data Science
  9. 2 Matrix Algebra and Random Vectors
  10. 3 Multivariate Analysis
  11. 4 Time Series Forecasting
  12. 5 Introduction to R
  13. 6 Introduction to Python
  14. 7 Algorithms
  15. 8 Data Preprocessing and Data Validations
  16. 9 Data Visualizations
  17. 10 Binomial and Trinomial Trees
  18. 11 Principal Component Analysis
  19. 12 Discriminant and Cluster Analysis
  20. 13 Multidimensional Scaling
  21. 14 Classification and Tree‐Based Methods
  22. 15 Association Rules
  23. 16 Support Vector Machines
  24. 17 Neural Networks
  25. 18 Fourier Analysis
  26. 19 Wavelets Analysis
  27. 20 Stochastic Analysis
  28. 21 Fractal Analysis – Lévy, Hurst, DFA, DEA
  29. 22 Stochastic Differential Equations
  30. 23 Ethics: With Great Power Comes Great Responsibility
  31. Bibliography
  32. Index
  33. End User License Agreement