Statistics and Data Visualisation with Python
eBook - ePub

Statistics and Data Visualisation with Python

  1. 516 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Statistics and Data Visualisation with Python

About this book

This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.

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Yes, you can access Statistics and Data Visualisation with Python by Jesus Rogel-Salazar in PDF and/or ePUB format, as well as other popular books in Social Sciences & Statistics for Business & Economics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Dedication Page
  7. Contents
  8. 1 Data, Stats and Stories – An Introduction
  9. 2 Python Programming Primer
  10. 3 Snakes, Bears & Other Numerical Beasts: NumPy, SciPy & pandas
  11. 4 The Measure of All Things – Statistics
  12. 5 Definitely Maybe: Probability and Distributions
  13. 6 Alluring Arguments and Ugly Facts – Statistical Modelling and Hypothesis Testing
  14. 7 Delightful Details – Data Visualisation
  15. 8 Dazzling Data Designs – Creating Charts
  16. A Variance: Population v Sample
  17. B Sum of First n Integers
  18. C Sum of Squares of the First n Integers
  19. D The Binomial Coefficient
  20. E The Hypergeometric Distribution
  21. F The Poisson Distribution
  22. G The Normal Distribution
  23. H Skewness and Kurtosis
  24. I Kruskal-Wallis Test – No Ties
  25. Bibliography
  26. Index