الإحصاء والاحتمالات لعلوم البيانات
For منهج تعليمي للإحصاء في علم البيانات, the strongest matches are camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers (This notebook-based textbook teaches Bayesian statistics and probability through), allendowney/thinkstats2 (Think Stats is a textbook that teaches statistics and) and allendowney/thinkbayes2 (Think Bayes is a set of Jupyter notebooks that). aloctavodia/statistical-rethinking-with-python-and-pymc3 and krasserm/bayesian-machine-learning round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
موارد تعليمية، ومكتبات، وأدوات تفاعلية لإتقان التحليل الإحصائي والاحتمالات في علم البيانات.
الإحصاء والاحتمالات لعلوم البيانات
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- camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers
CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
28,162عرض على GitHubThis project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for performing statistical inference and quantifying uncertainty through a probabilistic programming workflow using Python. The resource employs a computation-first pedagogy, teaching Bayesian methods and parameter estimation through executable code and simulations instead of formal mathematical notation. It provides a practical approach to implementing Markov Chain Monte Carlo sampling to estimate posterior distributions. The content covers building probabilistic models, integrating e
This notebook-based textbook teaches Bayesian statistics and probability through executable Python code and simulations, making it a focused learning resource for data science, though it lacks coverage of frequentist hypothesis testing and dedicated interactive visualizations.
Jupyter NotebookProbabilistic PriorsProbabilistic Modeling - allendowney/thinkstats2
AllenDowney/ThinkStats2
4,212عرض على GitHubThinkStats2 is a computational statistics course and educational library designed to teach probability and statistics through a programmatic approach. It provides a framework for studying statistical concepts by writing Python code and running simulations on real-world datasets. The project uses interactive notebooks and a collection of Python modules to deliver guided lessons. It emphasizes the verification of theoretical statistical laws through iterative computational experiments and simulation-driven testing. The resource covers broad capabilities in data analysis and data science traini
Think Stats is a textbook that teaches statistics and probability concepts with hands-on Python code examples in Jupyter notebooks, covering distributions, hypothesis testing, and Bayesian inference, making it a solid learning resource for data science.
Jupyter NotebookProbability DistributionsHands-On Labs - allendowney/thinkbayes2
AllenDowney/ThinkBayes2
2,051عرض على GitHubby Allen B. Downey
Think Bayes is a set of Jupyter notebooks that teach Bayesian statistics through hands-on Python exercises, covering probability distributions and Bayesian inference; it fits the search for an interactive statistics resource but focuses on Bayesian methods rather than including frequentist hypothesis testing.
Jupyter NotebookMathematics and Statistics - aloctavodia/statistical-rethinking-with-python-and-pymc3
aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3
This repository serves as an educational resource for Bayesian statistical modeling, providing a collection of instructional examples that translate theoretical concepts into executable Python code. It functions as a computational framework for performing statistical inference and parameter estimation, designed to help users learn and apply probabilistic programming techniques through interactive documentation. The project utilizes a probabilistic programming framework to define statistical models as directed acyclic graphs, enabling automated inference through advanced sampling algorithms. B
This repository provides Python/PyMC3 notebooks replicating the examples from the Bayesian statistics course "Statistical Rethinking," making it a valuable curated learning resource for Bayesian inference and probability distributions with Python integration, though it does not cover hypothesis testing or provide an interactive tool with hands-on exercises beyond running the code.
Jupyter NotebookBayesian Statistical Modeling - krasserm/bayesian-machine-learning
krasserm/bayesian-machine-learning
1,916عرض على GitHubThis project is an educational collection of computational notebooks and tutorials focused on Bayesian machine learning and probabilistic programming. It provides a framework for building predictive models that represent uncertainty by defining probability distributions over parameters rather than relying on single point estimates. The repository serves as a library of statistical methods for estimating parameter distributions, performing regression, and quantifying confidence levels in predictive systems. It covers a range of techniques including Gaussian process regression, Markov chain Mon
A set of Jupyter notebooks providing hands-on exploration of Bayesian machine learning, covering probability distributions and Bayesian inference through code and visualizations—a focused interactive resource for statistics concepts in data science, though narrower in scope than a general statistics primer.
Jupyter NotebookBayesian Machine LearningGaussian ProcessesMarkov Chain Monte Carlo Sampling - visualize-ml/book5_essentials-of-probability-and-statistics
Visualize-ML/Book5_Essentials-of-Probability-and-Statistics
3,675عرض على GitHubThis project is an educational resource providing a mathematical foundation in probability and statistics for machine learning. It offers a collection of interactive notebooks and textbooks designed to explain core statistical theories and data science principles through practical code examples. The content is structured into modular chapters that allow for self-paced learning of topics such as Bayesian inference and probability distributions. By utilizing browser-based execution and declarative visualization, the project enables users to manipulate variables and observe mathematical outcomes
This Jupyter Notebook-based book on probability and statistics offers interactive code examples in Python, covering core topics like probability distributions and hypothesis testing for data science.
Jupyter NotebookBayesian Inference
