Books

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.

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.

Tips and tricks for using d3.js (version 7), one of the leading data visualization tools for the web. It's aimed at getting you started and moving you forward. You can download for FREE or donate to encourage further development if you wish :-).

Is There a Six-Figure Freelance Developer in You?Leaving the Rat Race with Python shows you how to nurture, grow, and harness your work-from-home coding business online --- and live the good life!It's an insider's guide to freelance developing. Start your new thriving coding business now!

Readers will learn discrete mathematical abstracts as well as its implementation in algorithm and data structures shown in various programming languages, such as C, C++, PHP, Java, C#, Python and Dart. This book combines two major components of Mathematics and Computer Science under one roof.

Roger Peng and Hilary Parker started the Not So Standard Deviations podcast in 2015, a podcast dedicated to discussing the backstory and day to day life of data scientists in academia and industry. This book collects many of their conversations about data science and how it works (and sometimes doesn't work) in the real world.

A book about how to be a scientist the modern, open-source way.

This book describes the algorithms and procedures used to fit statistical models to data. The material covered is taught in the Advanced Statistical Computing course in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.

Modern AI applications are not built from scratch but, instead, by integrating off-the-shelf components: libraries, frameworks, and services. "Applied Artificial Intelligence - An Engingeering Approach" focuses on engineering user friendly, high-performance, and maintainable AI applications.

Setenta mil bits que pasan el test espectral del NIST, el criptoanálisis lineal completo sobre un millón de máscaras y la autocorrelación; los genera un registro de treinta y dos bits, y con sesenta y cuatro observados se predicen todos los demás. «Ruido blanco» nunca fue una propiedad de la señal: es una afirmación sobre el instrumento con el que la miramos. Este libro lleva el instrumental espectral de Telecomunicaciones a rodamientos rotos de laboratorio, a Bitcoin a cinco minutos en Polymarket y a la clave privada de una cartera de Bitcoin, y publica también los dos casos en que la respuesta es que ahí no hay nada.

Aprende a combinar SQL, Python, estadística y Machine Learning para priorizar posibles fraudes en retail. Un enfoque práctico, técnico y aplicado, con ejemplos, visualizaciones y códigos de apoyo.

Passage 1 — Chapter 9, "Data Security": a definition that sets the technical tone immediately A hacker is someone skilled at finding undocumented techniques and loopholes in the tangled architecture of complex information systems; by intent, a "white hat" looks for such loopholes in order to strengthen the system, while a "black hat" uses them to steal or to cause harm. Passage 2 — Chapter 14, "Metadata Management": an image that explains the whole topic in one paragraph A vivid illustration: an enormous document archive with no index at all — the shelves are full, but there is no way to find out what sits on them short of examining every single box by hand. That is exactly what an organization looks like when it has piled up mountains of data without also taking care of metadata — the information physically exists, but it cannot be used systematically, since the only way to find what is needed is to already know where it sits. Knowledge about data is always scattered: in a large company, one person carries the structure of a single database in their head, another the rules of a single integration, a third the history of a single metric, and nobody holds the complete picture. Passage 3 — Chapter 15, "Data Quality Management, Part 1": why "quality" is an empty word without a yardstick A postal address missing an apartment number works perfectly well for a mass catalog mailing and works terribly for a courier who has to knock on the right door — and in both cases it is the very same row in the very same database, only the yardstick applied to it differs. Passage 4 — Chapter 21, "Organizational Change Management": the book's closing summary Data governance, the coordinating hub of eleven knowledge areas this book opened with, stays an empty frame until specific people come to value the new way of working with data through their own experience — which is exactly why a book that began by mapping the circle of disciplines around that hub fittingly closes not with another technique or tool, but with a conversation about the person without whose deliberate participation no structure ever becomes a practice.

Passage 1 — Chapter 8, "Data Quality: Can the Board Trust Its Own Numbers": what bad data actually costs a leader Business folklore has long carried a caustic image: a machine that hands back garbage on the way out once it has been fed garbage on the way in — the old saying that poor input inevitably produces poor output, which this book already invoked earlier to explain why data quality went neglected for so long. It is worth taking that image seriously here and translating it into terms a leader can act on: exactly how much the input garbage costs a company, and by what test to recognize the moment a figure on a board slide becomes too risky to trust. Passage 2 — Chapter 9, "Analytics for Business: Data Warehousing and Metadata": a scene that stays with the reader Picture a board meeting: a single figure sits on the screen — quarterly revenue, say, or the share of customers who defected to a competitor — and one of the directors simply asks where that number came from. The pause that falls over the room while someone runs off to fetch an explanation from whoever put the report together says more about the state of trust in a company's analytics than any presentation ever could. Passage 3 — Chapter 11, "Data Management Maturity Assessment": maturity as something measured, not graded The practical conclusion for a leader is this: a maturity rung is not a verdict of "good" or "bad" — it measures how controlled and predictable a company's data work actually is. The higher the rung, the fewer surprises, and the more accurately a leader can forecast the consequences of decisions built on that data. Passage 4 — Chapter 13, "Managing Organizational Change in Data Management": the book's closing argument Taken together, the material across all thirteen chapters gives a leader a coherent view of data — from understanding it as a business asset to a concrete toolkit for leading organizational change. The architectural choices, technological infrastructure, and quality-control procedures examined earlier remain unrealized potential until someone at the most senior level of management personally takes on the work of carrying people through resistance and turning that potential into the company's everyday working habit.

This book presents a modern, unified, and language-agnostic workflow for creating analytical pipelines that are truly reproducible, from environment to execution.

Your company's answers are already written down. This book builds the machine that finds them.