Biostatistics study guides

ThinkStats2 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

This project is a data science curriculum and instructional syllabus designed to teach the fundamental principles and tools of the field. It provides a structured set of learning materials, including R programming courseware and guides for statistical learning. The materials focus on the practical application of data science, covering data cleaning, visualization, and exploratory data analysis. It includes resources for mastering specific techniques such as linear regression, classification, and unsupervised learning. The curriculum is organized into a modular sequence of educational modules

This project is a digital book directory and academic literature library. It serves as a curated collection of multidisciplinary texts and research materials organized by subject and publication year. The system uses a subject-based content catalog to arrange electronic books into specific domains. It employs visual markers and symbol-based encoding to distinguish between free and paid content. The directory provides capabilities for technical literature discovery and academic reading list management. Users can browse materials by subject and filter titles by recency using publication dates.

This project is a machine learning education resource consisting of Python implementations of statistical learning models and data analysis examples from a core textbook. It serves as a statistical modeling library that provides the code necessary to implement linear regression, classification, and unsupervised learning techniques for academic data analysis. The repository is structured as a reference-driven implementation, with a directory layout that mirrors the chapter and section hierarchy of the associated academic publication. It includes a set of scripts and notebooks designed to gener

This 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 project is a collection of interactive, command-line programming lessons designed for the swirl R package. It provides a structured curriculum for learning R programming and data science through a series of guided, self-paced exercises delivered via a command-line interface. The content covers a broad range of data science education, including language fundamentals, data cleaning and manipulation, statistical analysis, and data visualization. It also includes instructional modules focused on software development practices. These lessons are developed as a modular hierarchy of courses an

Statsmodels is a comprehensive Python library designed for statistical modeling, econometric research, and data analysis. It provides a robust framework for estimating and diagnosing a wide range of statistical models, enabling users to perform rigorous hypothesis testing, regression analysis, and complex data exploration within structured environments. The library distinguishes itself through its support for advanced statistical methodologies, including state space representation for dynamic systems and generalized linear frameworks that accommodate non-normal response variables. It offers s

This repository is the source for a curated collection of printable reference sheets for R and Python packages. It provides quick-reference guides organized by topic, available as both PDF files for offline printing and interactive HTML versions for online browsing. The collection also includes community-contributed translations of these reference sheets into multiple languages, expanding accessibility for non-English speakers. The project is built around a reproducible rendering pipeline that generates both PDF and HTML formats from source documents, using a lockfile to guarantee identical b

This project is an academic curriculum repository and educational resource center for studying probability, statistics, and machine learning. It serves as a deep learning course website and a hub for instructional materials, providing a structured collection of content designed to teach neural network architectures. The repository distinguishes itself by combining a comprehensive educational resource with a machine learning project archive. It provides a curated set of research examples and implementation guides for a wide range of models, including multilayer perceptrons, convolutional netwo

r4ds is a data science curriculum and educational resource designed for mastering the R programming language. It provides a structured learning path for the end-to-end process of importing, tidying, transforming, and visualizing data. The project emphasizes a reproducible data science guide and a comprehensive curriculum for data wrangling. It includes specialized tutorials on the grammar of graphics for layered data visualization and technical publications created with Quarto that blend executable code with narrative prose. The material covers a broad range of analytical capabilities, inclu