A Biomed Data Analyst Training Program

Modern Statistics: A Computer Based Approach with Python by Ron Kenett, Shelemyahu Zacks, Peter Gedeck Publisher: Springer International Publishing; 1st edition (September 15, 2022) ISBN-13: 978-3-031-07565-0 (hardcover) ISBN-13: 978-3-031-07568-1 (softcover) ISBN-13: 978-3-031-28482-3 (eBook). Buy at Amazon, Springer, Barnes & Noble Errata: See known errata here

Slides

1. Introduction
2. Data types and data integration
3. Supervised learning
4. Model performance
5. Time series
6. Data visualization
7. Causality and experimental design

Code and data files

This part of the repository contains:

• notebooks: Python code of individual chapters in Jupyter notebooks - download notebooks and data as notebooks.zip

The Python package mistat contains datafiles and utility functions referred to in the Modern Statistics book. It is available for installation from the Python package index https://pypi.org/project/mistat/. The mistat packages is maintained in a GitHub repository at https://github.com/gedeck/mistat.

Try the code

You can explore the code on Binder .

Installation instructions

Instructions on installing Python and required packages are here.

These Python packages are used in the code examples of Modern Statistics:

• mistat (for access to data sets and additional functionality)
• numpy
• scipy
• scikit-learn
• statsmodels
• pingouin
• xgboost
• KDEpy
• networkx
• scikit-fda
• pgmpy
• dtreeviz
• svglib
• pydotplus

The notebook InstallPackages.ipynb contains the pip command to install the required packages. Note that some of the packages may need to be pinned to specific versions.

If you have a problem with visualizing the decision tree or creating a network graph, follow the installation instructions for graphviz in the dtreeviz github site. On Windows, the problem is usually resolved by adding the path to the graphviz binaries to the PATH system variable.