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Foundations of Data Science with Python, by John M. Shea, teaches how to begin working with data, create visualizations, conduct statistical tests using resampling, perform analyses and make optimal decisions based on probability theory, and manipulate multi-dimensional data using linear algebra.

Jupyter Notebook 99.97% Python 0.03%

foundations-of-data-science-with-python's Introduction

Foundations of Data Science with Python (FDSP)

This book covers practical and mathematical foundations of data science, including:

  • data visualization
  • probability
  • statistics
  • linear algebra
  • application of these fundamentals to generate meaning from data.

FDSE is targeted at learners who have basic programming experience (preferably with Python) and knowledge of one-dimensional differential and integral calculus. It is designed to replace traditional courses on Engineering Statistics and Computation Linear Algebra.

The book can be read here: https://jmshea.github.io/Foundations-of-Data-Science-with-Python

FDSP provides an interactive experience with:

  • Interactive self-assessment quizzes via JupyterQuiz Animated GIF showing example interactive quiz via JupyterQuiz
  • Interactive flashcards to aid in learning terminology via JupyterCards Animated GIF showing the output of JupyterCards for a sample set of 3 cards

This repository is an experiment with writing in public. I will be putting the book here while it is still in very early and rough shape. My goals in doing this are to both offer this as a resource to the community and to get feedback from the community. Your feedback is welcome, but please do understand that this is very early and rough, so please be kind!

Copyright 2021 by John M. Shea. All rights reserved.

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