資料科學【免費】線上教科書整理

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更新日期:2023年2月28日


Math

• Learning Statistics with Python

連結:
https://ethanweed.github.io/pythonbook/landingpage.html

• Think Bayes 2

連結:
http://allendowney.github.io/ThinkBayes2/

• Welcome to Computational Discovery on Jupyter

連結:
https://computational-discovery-on-jupyter.github.io/Computational-Discovery-on-Jupyter/

• Numerical methods for partial differential equations

連結:
https://aquaulb.github.io/book_solving_pde_mooc/solving_pde_mooc/notebooks/01_Introduction/01_00_Preface.html

• Welcome to the Jupyter Guide to Linear Algebra

連結:
https://bvanderlei.github.io/jupyter-guide-to-linear-algebra/intro.html

• MAT244E STATISTICS

連結:
https://gulinan.github.io/mat244e/

• Statistics and Data Science

連結:

• Foundations of Data Science with Python

連結:
https://jmshea.github.io/Foundations-of-Data-Science-with-Python/intro/intro.html

• Computational and Inferential Thinking: The Foundations of Data Science

連結:
https://inferentialthinking.com/chapters/intro.html

• ND Pyomo Cookbook

連結:
https://jckantor.github.io/ND-Pyomo-Cookbook/README.html


Python

• Welcome to Python Packages!

連結:


資料科學

• Learning Data Science

連結:
https://www.textbook.ds100.org/intro.html

資料分析

• Python for Data Analysis, 3E

連結:

Data Visualization

• Visualization Curriculum

連結:
https://uwdata.github.io/visualization-curriculum/intro.html

• Exploratory Data Visualization with Altair

連結:
https://altair-viz.github.io/altair-tutorial/README.html

Machine Learning

• Machine Learning and Data Science Compendium

連結:
https://lazyprogrammer.me/mlcompendium/intro.html

深度學習

• Practical Deep Learning for Coders

連結:

• Dive into Deep Learning

連結:
https://d2l.ai/index.html

時間序列分析

• Forecasting: Principles and Practice (3rd ed)

連結:

備註:這本書的程式範例是使用R語言

Economics and Finance

以下有幾個連結看預覽好像一樣,其實是不同的網址。

• Python Programming for Economics and Finance

連結:

• Quantitative Economics with Python

連結:

• Continuous Time Markov Chains

連結:

• Coding for Economists

連結:
https://aeturrell.github.io/coding-for-economists/intro.html

• Introduction to Economic Modeling and Data Science

連結:

• LeDataSciFi-2023

連結:
https://ledatascifi.github.io/ledatascifi-2023/content/frontpage.html

Geographic Data Science

• Geographic Data Science with Python

連結:
https://geographicdata.science/book/intro

Graph Machine Learning

• Hands-on Network Machine Learning with Scikit-Learn and Graspologic

連結:
http://docs.neurodata.io/graph-stats-book/coverpage.html#hands-on-network-machine-learning-with-scikit-learn-and-graspologic

• Network Data Science

連結:
https://bdpedigo.github.io/networks-course/landing.html

Computer Vision

• OpenPifPaf Guide

連結:
https://openpifpaf.github.io/intro.html

• Algorithms for Automated Driving

連結:
https://thomasfermi.github.io/Algorithms-for-Automated-Driving/Introduction/intro.html

• Basics of Image Processing

連結:
https://vincmazet.github.io/bip/

Explainable AI

• Interpretable Machine Learning

連結:

• Explanatory Model Analysis

連結:

MLOps

• OK Transformer

連結:
https://particle1331.github.io/ok-transformer/intro.html

Interview

• The Data Science Interview Book

連結:

Others

• The Turing Way

連結:
https://the-turing-way.netlify.app/welcome

• Quality assurance of code for analysis and research

連結:
https://best-practice-and-impact.github.io/qa-of-code-guidance/intro.html

• The Good Research Code Handbook

連結:

更多線上書籍

連結:

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