Books

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.

Data Cleaning: The Ultimate Practical Guide is a guide to understanding what dirty data is, and how it gets into your dataset.This book will help you prevent most types of dirty data getting into your dataset, and clean out quickly and efficiently the remaining errors, so you can have clean, fit-for-purpose and analysis-ready data.

The book contains the full transcript of Software Diagnostics Services training with 25 hands-on exercises. This training course extends pattern-oriented analysis introduced in Accelerated Windows Memory Dump Analysis, Accelerated .NET Core Memory Dump Analysis, and Advanced Windows Memory Dump Analysis with Data Structures.

Most of the analytics tools available focus on assessing customer clicks, but have you ever thought about how little we know about what happens in between clicks? In this book we present methods, tools, and applications so that you can start uncovering the value of multiple interaction data available when people interact with computing systems.

Hypothesis Testing - The Simplest Guide In The Cosmos is a series of 6 short, snappy editable books that will help you reach the right conclusions about your data.These books make no assumptions about your previous experience and are perfect for beginners and those just getting started with hypothesis testing.

" Avaliação de sintomas clínicos categoria dos chatbots que atuam com avaliação de sintomas e indicações de melhores cuidados, possíveis diagnósticos. Atua na orientação de pacientes sobre os sintomas e possíveis ações quando o profissional de saúde não está disponível."

Unlock the world of Big Data with Enterprise Big Data Professional—your gateway to mastering the essential techniques and concepts driving today’s data revolution. This indispensable guide not only provides a thorough introduction to the core principles of Big Data but also serves as the official study resource for the APMG International certification program. Whether you're an aspiring data engineer or a seasoned professional looking to solidify your expertise, this book equips you with the knowledge and preparation needed to excel in the certification exam and advance your career. Dive in and transform your understanding of Big Data into actionable skills with this authoritative and insightful guide.

¿Estás buscando un libro con el que puedas aprender sobre aprendizaje profundo y PyTorch sin tener que pasar horas descifrando texto y código críptico? ¿Un libro técnico que sea también legible y entretenido? ¡Aquí lo tienes!

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.

Creating More Effective Graphs shows how to choose clear, accurate, effective graphs to make it easier to understand the data. It also shows how to avoid misleading and deceptive graphs. Some of the graph forms recommended are dot plots and trellis graphics (also called lattice plots and faceted plots). It contains examples of good and bad graphs.

D3 Start to Finish shows you how to build a custom, interactive and beautiful data visualisation using the JavaScript library D3.js (versions 6 & 7). The book covers D3.js concepts such as selections, joins, requests, scale functions, events & transitions. You'll put these concepts into practice by building a custom, interactive data visualisation.

Learn the fundamentals of HTML, SVG, CSS and JavaScript for building data visualisations on the web. Ideal if you're wanting to learn D3.js or you use Python and/or R and wish to get started with HTML, SVG, CSS and JavaScript. Straight to the point with lots of code examples.

Become a better data scientist by understanding different modeling mindsets.