Now in its second edition, this textbook provides an introduction to
Python and its use for statistical data analysis. It covers common
statistical tests for continuous, discrete and categorical data, as well
as linear regression analysis and topics from survival analysis and
Bayesian statistics.
For this new edition, the introductory chapters on Python, data input
and visualization have been reworked and updated. The chapter on
experimental design has been expanded, and programs for the
determination of confidence intervals commonly used in quality control
have been introduced. The book also features a new chapter on finding
patterns in data, including time series. A new appendix describes useful
programming tools, such as testing tools, code repositories, and GUIs.
The provided working code for Python solutions, together with
easy-to-follow examples, will reinforce the reader's immediate
understanding of the topic. Accompanying data sets and Python programs
are also available online. With recent advances in the Python ecosystem,
Python has become a popular language for scientific computing, offering
a powerful environment for statistical data analysis.
With examples drawn mainly from the life and medical sciences, this book
is intended primarily for masters and PhD students. As it provides the
required statistics background, the book can also be used by anyone who
wants to perform a statistical data analysis.