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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
• 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
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
• 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：