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Category: "R"

Books

  1. SIG avec R
    Gérer ses données spatiales avec R
    Jerome Mathieu

    Utilisez R comme un SIG pour gérer et cartographier vos données spatiales

  2. Credit Risk Modeling Working Notes
    A Collection of Presentations, Experiments, and Technical Papers
    Andrija Djurovic

    The Working Notes complement Applied Data Science for Credit Risk and Probability of Default Rating Modeling with R, offering practice-oriented insights. Based on the author’s GitHub repository, they address real-world challenges and are regularly updated to reflect ongoing developments.

  3. Data Preparation for Customer Analytics
    Steve Ludwig
    No Description Available
  4. Applied Data Science for Credit Risk
    A Practical Guide in R and Python
    Andrija Djurovic

    This book provides a practical guide to critical data science methods, focusing on their application in credit risk management. Using examples in R and Python, it presents step-by-step processes for applying various analytical techniques while highlighting the importance of aligning methods with the specific characteristics of the data. Designed for practitioners and those with foundational data science and banking knowledge, the book bridges theory and practice with real-world examples.

  5. Single Cell RNA-seq Analysis Using Public Data
    Getting Started with Single Cell RNA-seq Analysis in R
    LabCode
    No Description Available
  6. Data Science Machine Learning - The wine
    fortune.awala@iaue.edu.ng

    Unveil the Secrets of the grapes in the Vineyard! Join us on a journey through the world of wine like never before. Discover the magic of data science and machine learning as we uncork the mysteries hidden in the wine dataset. From predicting to redefining winemaking, get ready for a revolution. Stay tuned for a taste of tomorrow, today!

  7. R Bytecode
    An exploration of R's stack-based virtual machine and its bytecode.
    mikefc

    Disassemble and assemble R bytecode

  8. Probability of Default Rating Modeling with R
    Comprehensive overview of the modeling processes, principles, and designs
    Andrija Djurovic

    This book bridges theory and practice in PD rating modeling, offering practical steps, real-world examples, and a focus on design. It enables readers to shape customized solutions for diverse institutions, transforming the landscape of credit risk modeling.

  9. The Hitchhiker's Guide to Linear Models
    Based on the famous R programming language
    Mauricio 'Pacha' Vargas Sepúlveda

    For every exercise I did my best to connect the specific statistical concepts with R code, and every time I use linear algebra I connect it with a concrete R example. In this book you will not find something such as "this is left as an exercise to the reader".

  10. Reinforcement Learning Explained - A Step-by-Step Guide to Reward-Driven AI
    Luka Nikolic

    Discover the thrilling world of Reinforcement Learning (RL) in our engaging eBook! Learn the fundamentals of RL, from AI agents and environments to rewards and actions. Explore real-world applications like robotics, healthcare, and personalized recommendations, where RL is transforming industries.

  11. Building reproducible analytical pipelines with R
    Bruno Rodrigues, Phd

    Build reproducible analytical pipelines to output consistent, high-quality data products using R, Github and Docker. Learn about functional and literate programming to keep your code concise, easier to test and share and easily understandable by others by packaging it. Run your pipelines on Github Actions and focus on what matters: analysing data!

  12. Longitudinal Data Analysis Using R
    Alexandru Cernat

    The book covers all the key skills needed for preparing, exploring, and analysing longitudinal data. To facilitate understanding and help readers learn these skills, it interweaves statistical modelling with computer code and visualizations. It does this using real-world data, code, and outputs that readers can replicate.

  13. Biological Data Science with R
    Stephen Turner

    Biological Data Science with R covers data manipulation with dplyr, visualization with ggplot2, essential statistics, survival analysis, RNA-seq analysis, phylogenetic trees, predictive modeling and infectious disease forecasting, text mining and natural language processing, and more.

  14. An Educator’s Guide to the Open Case Studies
    A Guide for using Example data analyses with real-world data inside and outside the classroom
    Carrie Wright, Stephanie Hicks, Lyla Atta, and Michael Breshock

    If you are an independent learner or an instructor for a data science, statistics, or public health course, check out the open case studies project (www.opencasestudies.org) and this guide which will describe the variety of ways our case studies can be used for hands-on data science activities.

  15. The Hitchhiker's Guide to Responsible Machine Learning
    The introduction to Interpretable and Responsible Machine Learning and eXplainable Artificial Intelligence with code examples for R
    Przemysław Biecek

    Selected modern machine learning techniques and the intuition behind them. Methods are supplemented by code snippets with examples in R language. The process is shown through a comic book describing the adventures of two characters, Beta and Bit.  See the flipbook version at https://betaandbit.github.io/RML/