Data Science

Class imbalance isn’t a problem. Poor methodology is. This book challenges outdated practices and provides rigorous, data-driven alternatives. We focus on selecting the right tools, threshold tuning, real costs (not class frequencies), and strategic evaluation metrics, to build models that work.

Unlock the power of YAML with this comprehensive guide! Whether you're just starting or looking to enhance your skills, From Zero to YAML offers step-by-step instructions, practical examples, and real-world applications to help you master this essential tool for DevOps, cloud infrastructure, and configuration management. Learn how to write, structure, and manipulate YAML files with ease, and apply it to modern technologies like Docker, Kubernetes, AWS, and more. Ready to streamline your workflows and optimize your configurations? Dive into the world of YAML and transform the way you work with data!

The book provides a modern look at introductory Biostatistical concepts and the associated computational tools using the latest developments in computation and visualization in the R language environment. The book includes practical data analysis based on datasets that can be downloaded here: https://github.com/muschellij2/biostatmethods.

Mathematical Foundations of AI and Data Science: Discrete Structures, Graphs, Logic, and Combinatorics in Practice transforms abstract mathematical concepts into practical tools for computational problem-solving.Explore logic, set theory, relations, functions, combinatorics, discrete probability, graph algorithms, trees, algebraic structures, Boolean systems, recurrence relations, optimization.

This book teaches you to use R to effectively visualize and explore complex datasets. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modeling strategies. This book is based on the industry-leading Johns Hopkins Data Science Specialization.

This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science. Printed copies of this book are available through Lulu.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

Recent advancements in causal inference have made it possible to gain profound insight about our world and the complex systems which operate in it. Industry professionals and academics in every domain ask questions of their data, but traditional statistical methods often fall short of providing conclusive answers. This is where causality can help.

A beginner-friendly introduction to machine learning with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of artificial intelligence, by easily training and deploying their ML models!

The official companion of Finding Hidden Messages in DNA, the popular first course in Coursera's Bioinformatics sequence. Learn how biologists have begun to decipher the strange and wonderful language of DNA without needing to put on a lab coat. This book contains the first two chapters from Volume 1 of Bioinformatics Algorithms: An Active Learning Approach.

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. This book covers the design, acquisition, and analysis of fMRI data.

Learn everything there is to know about Shodan from the founder himself. The book covers all aspects from the website through to the developer API with exercises to help test your understanding.

Modern Computational Statistics with R teaches statistics as a disciplined way of thinking: start with the scientific question, design, data, and uncertainty before reaching for formulas. Through real examples, simulations, and R, readers learn how to turn data into defensible evidence and build the statistical foundations needed for modern data science, machine learning, and AI.

This book gives a brief, but rigorous, treatment of statistical inference intended for practicing Data Scientists.